MODELADO PARA LA EVALUACIÓN DEL RENDIMIENTO …

Universidad de Málaga

Escuela Técnica Superior de Ingeniería de Telecomunicación

Tesis Doctoral

MODELADO PARA LA EVALUACIÓN DEL RENDIMIENTO ENTRE EXTREMOS

DE LA CALIDAD DE SERVICIO “STREAMING” SOBRE REDES CON ACCESO INALÁMBRICO

Autor

Gerardo Gómez Paredes Ingeniero de Telecomunicación

Directores

José Tomás Entrambasaguas Muñoz Mª Carmen Aguayo Torres Doctor Ingeniero de Telecomunicación Doctora Ingeniera de Telecomunicación

Año 2009

Abstract ix

ABSTRACT

This thesis addresses the issue of the end-to-end Quality of Service (QoS) evaluation over wireless access networks. A generic approach is proposed to model and evaluate the end-to-end QoS for data services over broadband wireless networks. Concretely, the end-to-end performance has been characterized by a cumulative degradation of the quality throughout the different network elements, interfaces and protocol layers. The presented semi-analytical model has been distinguished for streaming services, though it might be easily extended to any other data service.

The proposed QoS model provides a set of performance indicators (throughput, delay and loss rate) at the different protocol layers that composes the streaming service architecture. This model provides a clear understanding of the main factors that are limiting the application level performance, as well as the QoS requirements at the different protocol layers under certain network conditions.

At the physical layer, a variable-rate multiuser and multichannel subsystem is modeled. This subsystem is based on OFDM technology and uses adaptive modulation and channel coding functionalities (similar to 3GPP-LTE technology); nevertheless, the proposed methodology allows to extend the performance evaluation process to any other type of wireless environments (GPRS, UMTS, WLAN, WiMax, etc.) by applying the appropriate models.

At the MAC sublayer, different multiplexing strategies (RR, BC, PF, LDF, BC-LDF, M-LWDF) have been analyzed over a multiuser and multichannel environment. It has been shown (analytically and from simulations) that those algorithms that adapt its allocation criteria to the instantaneous channel conditions are able to take advantage of the multiuser and multichannel diversity gain. In addition, it has been also verified that the best performance is obtained by those algorithms that consider both the channel conditions and QoS indicators. In this sense, M-LWDF is the best performing algorithm in terms of fairness between users and capacity (reaching to a 50% more of capacity than Round Robin for uncorrelated channels).

At the RLC sublayer, an analytical model of a retransmissions mechanism (ARQ-like) is provided to evaluate its impact on the throughput, delay and loss rate. It has been shown how the use of retransmissions allows to limit the loss rate at the expense of decreasing the throughput and increasing the delay. The maximum number of retransmissions Nrtx at RLC layer becomes an important parameter to optimize the overall performance. In order to achieve an optimum performance of upper layers and fulfill the QoS requirements, the following Nrtx values have been obtained assuming a target bit error rate of 10-4: Nrtx = 6 (for TCP and TFRC) and Nrtx = 3 (for UDP).

At the PDCP sublayer, the functionalities of segmentation and reassembly as well as robust header compression (ROHC) have been modeled. According to the analytical results, the use of this technique allows to increase the user throughput up to a 7% (assuming a mean packet size of 480 bytes at application level) for a constant number of users in the system; or equivalently, the system admits a higher number of users

x Abstract

maintaining the same quality of service for each of them. This gain value is greater as the packet size diminishes.

At the network layer, the impact of a fixed IP-based access network is studied. A set of IP multiplexing algorithms (RR, LDF, WRR and WFQ) have been analyzed. Simulation results show a similar performance among multiplexing algorithms whenever the traffic generated by the different users is homogenous (similar source rate); otherwise, WFQ is the only technique that provides a fair treatment among the different users sharing the resources since this technique considers the throughput, delay and variable packet size requirements. Anyhow, the impact of the fixed IP domain on the end-to-end IP performance (between terminal and server) is small since the radio interface is assumed to be bottleneck of the network.

Regarding the transport layer, UDP, TCP and TFRC protocols have been modeled and evaluated. Analytical results have shown that:

• When congestion control is not used (e.g. for UDP), a high loss rate in the network may occur for high load conditions, which is unacceptable for streaming services.

• When an end-to-end congestion control is applied, the number of retransmissions at RLC level is a key factor to optimize the performance.

• TCP can be an appropriate protocol for the transmission of streaming services whenever the network conditions (in terms of delay and loss rate) are good. Otherwise, TCP behavior is very influenced by these factors, mainly by packet losses.

• End-to-end TCP delay is very influenced by the loss rate in lower layers, as TCP reacts to congestion by reducing the transmission window and performing end-to-end retransmissions. In order to minimize the loss rate coming from the radio interface, a higher number of RLC retransmissions could be performed. Although RLC retransmissions lead to a higher delay in the radio subsystem, there is a range of Nrtx values that reduces the end-to-end delay.

• As the BERT is lower, the potential TCP throughput achieves its maximum value for a lower Nrtx value, since the physical layer provides higher reliability. However, the impact of a lower BERT on the TCP delay depends on the load level, leading to lower TCP delays for low load and higher TCP delays for high load.

• Too small values of the maximum congestion window (W) do not allow to fully utilize the resources, thus reducing the maximum throughput. On the other side, too large values of W requires a high reliability (in terms of loss rate) to use the whole window; thus, too many RLC retransmissions are required, which increases the end-to-end delay.

• The values of BERT, Nrtx and W parameters must be jointly decided, having a trade off between throughput and delay. For a given BERT = 10-4, Nrtx has been set to 6 in order to limit the end-to-end delay. With these values of BERT and Nrtx, the maximum TCP window that maximizes the throughput is W = 32 kB.

Abstract xi

• TFRC slightly improves TCP performance despite it is not an error free protocol. Nevertheless, the residual error rate obtained by TFRC can be kept under a certain threshold if radio parameters are properly configured.

• In low load conditions, the three transport protocols obtain a similar performance for streaming services. However, for high load, TCP and TFRC decrease their sending rate in order to avoid overloading the network queues.

An end-to-end real-time emulation platform has been developed to validate the proposed QoS model. This platform is based on a streaming client-server architecture, which uses partial results from the radio interface to simulate the impact of this subsystem. That way, end-to-end real time results have allowed to evaluate the accuracy of the analytical model and, additionally, it allows to experience the real time service quality.

xii Acrónimos

ACRÓNIMOS

1xEV-DO 1X-Evolution Data Optimized

2G Second Generation

3G Third Generation

3GPP Third Generation Partnership Project

4G Fourth Generation

ACK Acknowledgement

ADWN Advertised Window

AM Acknowledged Mode

AQAM Adaptive Quadrature Amplitude Modulation

ARQ Automatic Repeat reQuest

AWGN Additive White Gaussian Noise

BC Best Channel

BDP Bandwidth-Delay Product

BLER Block Error Rate

BPSK Binary Phase Shift Keying

CDMA Code Division Multiple Access

CSI Channel State Information

DUPACK Duplicated Acknowledgement

E-DCH Enhanced Dedicated Channel

EDGE Enhanced Data for Global Evolution

FIFO First-In-First-Out

FQ Fair Queuing

FTP File Transfer Protocol

GPRS General Packet Radio Service

GPS Generalized Processor Sharing

GSM Global System for Mobile Communication

HARQ Hybrid Automatic-Repeat-reQuest

HSDPA High Speed Downlink Packet Access

HSPA High Speed Packet Access

HSUPA High Speed Uplink Packet Access

HTML HyperText Markup Language

Acrónimos xiii

HTTP HyperText Transfer Protocol

IETF Internet Engineering Task Force

IP Internet Protocol

ISI InterSymbol Interference

KPI Key Performance Indicator

LDA Loss Differentiation Algorithms

LDF Largest Delay First

LDF-BC Largest Delay First to Best Channel

LTE Long Term Evolution

MAC Medium Access Control

MIMO Multiple-input Multiple-output

M-LWDF Modified Largest Weighted Delay First

OFDM Orthogonal Frequency Division Multiplexing

PDCP Packet Data Convergente Protocol

PDU Packet Data Unit

PF Proportional Fair

PHY Physical

PoC Push-to-Talk over Cellular

PRB Physical Resource Block

QAM Quadrature amplitude modulation

QoS Quality of Service

QPSK Quadrature Phase Shift Keying

RFC Request for Comments

RLC Radio Link Control

ROHC Robust Header Compression

RR Round Robin

RTCP Real Time Control Protocol

RTO Retransmission Time-Out

RTP Real-Time Transport Protocol

RTSP Real-Time Streaming Protocol

RTT Round Trip Time

SDM Space Division Multiplexing

xiv

SDP Session Description Protocol

SNR Signal-to-Noise Ratio

TCP Transmission Control Protocol

TDMA Time Division Multiple Access

TFRC TCP Friendly Rate Control

TTI Transmission Time Interval

UDP User Datagram Protocol

UMTS Universal Mobile Telecommunications System

URL Uniform Resource Locator

VBR Variable Bit-Rate

VoIP Voice over IP

WCDMA Wideband Code Division Multiple Access

WFQ Weighted Fair Queuing

WML Wireless Markup Language

WRED Weighted Random Early Detection

WRR Weighted Round Robin

QOS MODELING FOR END-TO-END STREAMING PERFORMANCE EVALUATION OVER WIRELESS ACCESS NETWORKS

ABSTRACT

This Ph. D. thesis presents an end-to-end Quality of Service (QoS) model to evaluate the performance of data services over wireless access networks. The proposed model is based on a bottom-up approach, which considers the cumulative performance degradation along each network element and protocol layer. This approach allows predicting the performance of different services under specific environments. As representative case, a scenario where a set of mobile terminals connect to a streaming server through an All-IP radio access network has been modeled. UDP, TCP and the new TCP-Friendly Rate Control (TFRC) protocols have been analyzed at the transport layer. The radio interface consists of a variable-rate multiuser and multichannel subsystem, including retransmissions, dynamic resource allocation and adaptive modulation and coding. Numerical parameters at the physical layer follow the 3GPP Long Term Evolution (LTE) specifications. By means of both analytical and simulation results, a streaming service is analyzed and its main quality indicators have been evaluated at different domains and layers in the protocol stack. The proposed analytical QoS model is validated by measurements from a real-time end-to-end emulator.

1. INTRODUCTION

In the last years, wireless and wired networks have merged together to provide seamless resource connectivity. This wired-wireless connectivity is intended to support packet data services with different Quality of Service (QoS) requirements. Usually the

184 QoS Modeling for End-To-End Streaming Performance Evaluation over Wireless Access Networks

wireless subsystem is the bottleneck of the whole network, since radio bandwidth is low, packet delay is large and the number of errors is high compared to those in wired networks. However, the end-to-end QoS must be ensured along the whole network in order to achieve the desired service quality for the end user. Thus, the assessment of the service performance requires analyzing the performance of the whole network (from user equipment to application server or remote user equipment).

Traditionally, network metrics like accessibility, retainability and quality were sufficient to evaluate the user experience for voice services. However, for data services, the correlation between network metrics and user performance is not so straightforward due to the following reasons: firstly, data systems have several layers of protocols; and secondly, radio data bearers are typically shared among different applications. In these conditions, the challenge of data service performance assessment is usually addressed through active terminal monitoring over real networks [Gómez, 2005]. Obviously, this is expensive and very time consuming if the operator wants to collect statistics on a reasonable number of terminals, applications and locations.

QoS over wireless access networks is a popular research topic. However, many previous research works have focused on the same administrative domain and/or layer in the protocol stack. A number of papers have dealt with new congestion control schemes for wireless multimedia at the transport layer (TCP-friendly) [Floyd, 2003] or QoS-scheduling techniques at the radio interface [Andrews, 2002][Ryu, 2005][Kaur, 2008][Bokhari, 2008]. A few of these works describe a general framework for end-to-end QoS control [Montes, 2002][Gao, 2004][Zhang, 2005][Pliakas, 2008] or include cross-layer proposals for end-to-end QoS management over wireless networks [Zhang, 2005][Liu, 2006][Ci, 2008].

In this Ph. D. thesis, the problem of providing end-to-end performance estimations over a wired-wireless network is addressed by means of QoS models. Packet data service performance is characterized as the cumulative performance degradation along each network element and protocol layer, including interfaces and behavior of the protocols. We follow a bottom-up approach, from the physical up to the application layer. Such method allows predicting the performance of different services (even non-existing ones) under specific wireless environments without the need of running a real application.

Probably, streaming service is nowadays the most challenging application due to its high bandwidth and strict delay requirements. Thus, we qualify the proposed model for mobile terminals connecting to a streaming server through an All-IP radio access network. The impact of the user multiplexing algorithm at the Medium Access Control (MAC) layer on the end-to-end the service performance is studied. In addition, we pay attention to different transport layer protocols for the user plane (UDP, TCP and TCP-Friendly Rate Control (TFRC) [Floyd, 2003]).

2. System Model 185

2. SYSTEM MODEL

This section presents the scenario and protocol stack under analysis. As aforementioned, a streaming service has been selected as representative case (see Figure 1). The system is divided into two subsystems: the radio access network and the wired network. An access node is the network element responsible for interconnecting both subsystems in order to provide an end-to-end connection between the User Equipment (UE) and streaming server.

User Equipment Access Node Streaming Server

UserPlane

SignalingPlane

UserEquipments

StreamingServer

Access Node

IP RoutersUDP TCPTFRC

*TransportProtocol

UserPlane

SignalingPlane

RTSP

SDP

RTP / RTCP

TCP Transport Prot.*

Audio/video

PHY

IPNetwork

RTSP

SDP

RTP / RTCP

TCP

Audio/video

Transport Prot.*

L2/L1L2/L1

SL5

SL4

SL2c

SL2a

SL2b

L1

L2a

L2b

L2c

L3

L4

L5

SL3

{R,D,P}L5

{R,D,P}L4

{R,D,P}L3

{R,D,P}L2b

{R,D,P}L1

{R,D,P}L2c

Application -

Transport -

Network -

Link

Physical -

IP IPIP

RLC

MAC

PDCP

{R,D,P}L2a

MAC

PDCP

RLC

PHYSL1

Figure 1. Scenario and protocol stack under analysis

Along the protocol stack, Packet Data Units (PDUs) of Layer i (Li) are hereinafter referred to as Li-PDUs. The size of the PDUs at each layer is denoted by BLi and the Li-PDU header length is denoted by HLi. The following terminology has been used for the performance indicators:

• SLi: mean information rate offered to layer i.

• RLi: mean net throughput achieved at layer i (at the receiver).

• DLi: mean Li-PDU delay.

• PLi: mean Li-PDU loss rate.

A description of the system model is given from Layer 1 (L1) up to Layer 5 (L5):


L1) A variable-rate multiuser and multichannel subsystem has been considered for the radio interface. Channel multiplexing is performed at the PHYsical (PHY) layer, that is, the radio channel is divided into resources to be independently allocated to users. Moreover, PHY layer applies adaptive modulation and channel coding [Goldsmith, 2005]. These techniques select the data rate and redundancy according to the instantaneous quality of the channel, i.e. high data rates are transmitted with good Signal-to-Noise Ratio (SNR) whereas more robust schemes (or even no transmission) are used in fading periods. That way, throughput is maximized while keeping the received Bit Error Rate (BER) under certain target value.

L2) Link layer is responsible for performing user multiplexing, i.e. resources are assigned to users following a specific user multiplexing algorithm. Moreover, selective retransmissions of erroneous L2-PDUs (if so configured) as well as compression of upper layer headers are carried out at this layer. Traffic shaping is performed at the upper interface of the network side L2; when the network load is high, data may be lost due to queue overflow.

L3) An all-IP radio access network has been considered at the network layer (L3), through which mobile terminals connect to the streaming server. Average queuing delays, throughput and loss rates in the routers have been analyzed.

L4) At the transport layer (L4), TCP has is used for the signaling plane (see Figure 1) while several options are analyzed for the user plane (UDP, TCP and TFRC).

L5) At the application signaling plane, Real-Time Streaming Protocol (RTSP) and Session Description Protocol (SDP) is assumed. Signaling protocols are used to establish and control the streaming session1. At the user plane, the Real-time Transport Protocol (RTP) carries delay-sensitive data while the Real-time Transport Control Protocol (RTCP) conveys information on the participants and monitors the quality of the RTP session.

In this work, throughput, delay and loss rate indicators at the application layer have been analyzed, assuming a large enough application buffer to mask the network jitter at this layer. As regards the traffic model, variable rate information sources have been considered. Mean source rate per user at application layer (SL5) has been kept constant while the number of users in the system was increased. A summary of the numerical parameters employed along this work at all layers is provided in Table 2.

1 Performance analysis of streaming signaling protocols during session setup is out of the scope of this Ph. D. thesis; however, further details can be found in [1][4].

3. Physical Layer Model 187

3. PHYSICAL LAYER MODEL

Results in this work are shown for an Orthogonal Frequency Division Multiple Access (OFDMA) scheme, similar to that in 3GPP Long Term Evolution (LTE) [Gómez, 2009][3GPP TS 36.201]. In LTE technology, radio resources are structured in slots of length Tslot = 0.5 ms. The Transmission Time Interval (TTI) is set to 1 ms, thus containing 2 slots. Each slot can be seen as a time-frequency resource grid composed by OFDM symbols along the time. Each slot is divided into a number of Physical Resource Blocks (PRBs) consisting of 12 consecutive sub-carriers along 7 consecutive OFDM symbols. Assuming a system bandwidth BW = 10 MHz, there is a total of 600 data subcarriers per OFDM symbol, so the number of PRBs is 600/12 = 50. This number will be hereinafter considered as the number of “channels” Nc = 50.

According to LTE specifications, the first OFDM symbols (from 1 to 3) in a TTI are reserved for control channels (signaling). A static configuration of the control channel (allocated into the first two OFDM symbols of each TTI) has been assumed, being the remaining OFDM symbols in the TTI used to transmit user data. The minimum allocable unit to a user is formed by two consecutive PRBs along the time, as shown in Figure 2.

Frecuency

PRB #1

600 data subcarriers (10 MHz)

Slot #1

(7 OFDM Symbols)

12 SubcarriersT

ime

PRB #2 PRB #50

Data

Signaling

(2 OFDM symbols)

PRB #1 PRB #2 PRB #50

Slot #2

(7 OFDM Symbols)

TB = TTI

Mc = 12

Subcarrier

MT = 12

Minimum

allocable unit

Nc = 50 channels

Figure 2. LTE physical resources structure

LTE physical resources structure has been modeled by the following set of parameters (shown in red color in Figure 2): frame2 length TB = TTI = 1ms, number of channels Nc = 50, number of subcarriers per channel (in the frequency domain) Mc = 12 and number of subcarriers per frame (in the time domain) MT = 12. Hence, the minimum

2 The concept of “frame” used in this abstract is called “subframe” in LTE specifications.


allocable unit includes a maximum of Mc x MT = 144 subcarriers. Further details on the numerical parameters at the physical layer can be found in Table 2 at the end of this extended abstract.

The received instantaneous SNR is considered to be invariant within a minimum allocable unit (composed by Mc·MT QAM symbols). A Nu·Nc matrix γ[n] = {γi,k[n]} represents the received instantaneous SNR at the frame n for each pair {user i, channel k}. The channel state is estimated by each user and assumed to be fed back to the access node with negligible error or delay. A decreasing exponential model is assumed for SNR correlation between channels of the same frame [Entrambasaguas, 2007], modeled by a correlation parameter ρc; this parameter is a function of the frequency distance between contiguous channels and the coherence bandwidth of the channel. Time-correlation between frames follows the Jakes’ model [Goldsmith, 2005], assuming a terminal speed v = 4 km/h.

Adaptive Quadrature Amplitude Modulation (AQAM) has been assumed at the physical layer, i.e. the transmission rate tracks the channel quality in order to keep the BER below a desired target (BERT). In ideal conditions, BERT will be an upper limit of the instantaneous BER at the physical layer. A constellation of Ri symbols is employed within the fading region [γi-1, γi); i = 0,1,…,R (defining γ-1 = 0). Commonly, R0 = 0 and γ0 is termed as SNR cutoff. The set of thresholds {γi} are designed to fulfill a target BER (BERT). The following set of modulation modes has been used: no transmission (NTX), QPSK, 16QAM and 64QAM. Let mik[n] be the potential rate (in bits/symbol) that user i might achieve through subcarrier k at certain time instant n. mi,k[n] can only take a finite number of integer values given by the aforementioned modulation modes, i.e. 0, 2, 4 and 6 bits/symbol. As all subcarriers within a minimum allocable unit are assumed to experience the same instantaneous SNR, all subcarriers will use the same modulation scheme mi,k[n] (in bits/symbol) for that frame. Modulation scheme will be adapted on a frame rate. Channel coding reduces the value of the thresholds in a quantity related to the coding gain that generally ranges from 2 to 10 dB. We have assumed a turbo coder with coding rate 1/3, which achieves an average coding gain Gcod ≈ 8 dB.

Resources are assigned to users (at MAC layer) based on different criteria, like the potential rate that a user may instantaneously achieve. Let ri,k[n] be the potential rate (in bits/frame) that user i might achieve through channel k at frame n. Then, the average potential rate (in bits/frame) that user i might achieve can be computed from the aggregation of along the Nc channels. A summary of the performance indicators at the physical layer is shown in Eq (1).

PHY Model

1L TP BER≤

1 ,1

cNiL i k

kR r

=

= ∑ , , ,[ ] [ ]i k c T i kr n M M m n= ⋅ ⋅ , , ,[ ] ( [ ], , )i k i k T codm n f n BER Gγ=

1LD TTI≈

(1)

1iLR,i kr

4. Link Layer Model 189

4. LINK LAYER MODEL

The link layer includes the Medium Access Control (MAC), Radio Link Control (RLC) and Packet Data Convergence Protocol (PDCP) sub-layers (as shown in Figure 1).

4.1. MAC Layer

A set of Nu users share the radio transmission resources. The MAC layer at the access node allocates channels to users on a frame basis, i.e. for each new frame, the system assigns each physical channel (PRB) to a single user. The user multiplexing algorithm has one of the most remarkable effects on the whole system performance.

The actual number of bits extracted from the ith user queue and allocated on channel k, denoted by Ri,k[n], will be 0 or rik[n] depending on the user multiplexer decision. The total number of bits extracted the ith user queue at frame n is given by

{ }2 ,1

[ ] [ ]cN

L a i kk

R user i n R n=

= ∑ (2)

Six different multiplexing algorithms have been evaluated:

• Round Robin (RR) [Hanssen, 2004] is fair among users although it can get no multiuser or multichannel diversity gain.

• BC (Best Channel) [Knopp, 1995] assigns the radio resources to the users with the highest potential rate rik[n]. BC will get the highest throughput for some users but at the expense of some others’ starvation.

• Proportional Fair (PF) [Viswanath, 2002] is similar to BC, though the average SNR is algo taken into account.

• Largest Delay First (LDF) [Entrambasaguas, 2007] is adaptive to the delay, providing the transmisión turn to the users who has experienced the highest waiting time.

• Largest Delay First to Best Channel (LDF-BC) [Entrambasaguas, 2007] improves LDF algorithm because its decision is based on both delay and instantaneous channel conditions

• Modified Largest Weighted Delay First (M-LWDF) [Andrews, 2002]: considers both channel quality and QoS indicators in its multiplexing criteria by allocating the resources to the user with higher potential rate and delay product. According to [Andrews, 2002], M-LWDF algorithm is throughput optimal, i.e. it gets the maximum possible diversity gain for stable queues.


A classification of the evaluated multiplexing algorithms is shown in Table 1.

Table 1. Classification of the multiplexing algorithms

Adaptive Algorithms Fixed Algorithms Adaptive to the channel Adaptive to the delay

RR PF LDF LDF-BC M-LWDF BC

Regarding the reliability results, error rate at MAC layer (L2a) depends on the target BER (BERT) configured at the physical layer and the size of an L2a-PDU (BL2a). In order to provide an expression for the Block Error Rate (BLER) at MAC layer, i.e. PL2a, we have assumed that instantaneous BER is equally distributed along bits3. In this case, the contribution of erroneous L2a-PDUs due to bit errors at the radio interface is given by:

( ) 2

2 1 1 L aBL a TP BLER BER= ≤ − − (3)

MAC layer results have been obtained from simulations. Layers above MAC will use MAC results as inputs to evaluate analytically the performance at upper layers.

It has been proved (analytically and from simulations) that those algorithms whose decision takes into account the instantaneous channel conditions are able to provide certain diversity gain. However, the best performance is achieved when both channel conditions and QoS indicators are considered in the decision. In that sense, M-LWDF algorithm outperforms the others in terms of fairness and capacity (obtaining a 50% higher capacity than Round Robin when channels are uncorrelated).

MAC Model

( ) 2

2 1 1 L aBL a TP BER≤ − −

[ ] [ ] [ ],2 , ,

1

if channel is assigned to user,

0 otherwise

Nci k

L a i k i kk

r n k iR R n R n

=

⎧⎪⎪= =⎨⎪⎪⎩∑

2 ( )L aD from simulation

(4)

3 This assumption is only true for BPSK and 4-QAM, but a proper interleaving makes this assumption reasonably valid if adjacent coded bits are mapped alternately onto less or more significant bits of the constellation, thus avoiding long runs of lowly reliable bits.


4.2. RLC Layer

While some streaming applications are error-tolerant, others may require reliable data delivery. In that case, the network can optionally retransmit erroneous L2b-PDUs, which is commonly known as Acknowledged transmission Mode (AM). RLC layer is responsible for the segmentation and reassembly of upper layer PDUs and for performing optional selective retransmissions. Thus, the error rate can be lowered by means of retransmissions at the expense of decreasing the throughput as well as increasing the mean delay and jitter.

The retransmission mechanism analyzed in this Ph. D. thesis considers a generic link layer retransmission scheme (based on the ARQ protocol) [Peisa, 2001]. ARQ protocol behavior is presented in Figure 3 and described as follows. Incoming upper layer PDUs are segmented into Nr L2b-PDUs (usually named radio blocks) and stored in a buffer. The transmitter sends all L2b-PDUs and polls the receiver in the last L2b-PDU of a higher layer PDU (L2c-PDU). A status report request is issued if no response is received to the polling upon expiration of Tw_stat. Selective acknowledgement is used to report which L2b-PDUs were incorrectly received. Non-acknowledged L2b-PDUs are retransmitted if the maximum number of retransmissions has not been reached; we call cycle i the ith (re)transmission attempt. The delay until a status report is received after the last polling (or status report request) is denoted by Tstat.

x

x

x

Request

n0 PDUs

Tstat

Tw_stat

Cycle 0

Cycle 1

Cycle 2

n1 retransmissions

n2 retransmissions

Report (n1 errors)

Report (n2 errors)

(without errors)

Report (n2 errors)

Access Node

UserEquipment

Figure 3. ARQ mechanism

4.2.1 RLC Loss Rate

Assuming a maximum number of retransmission attempts Nrtx, the loss rate PL2b is given by the probability that an L2b-PDU is not correctly received after Nrtx retransmissions, i.e. .

12 2

rtxNL b L aP P +=


4.2.2 RLC Throughput

The average throughput at RLC layer, RL2b, can be computed from the throughput achieved at the layer below (MAC), RL2a. We must take into account that MAC layer throughput comes from the aggregation of two types of PDUs: data and control L2b-PDUs. Hence, RL2a can be expressed as:

2 2 2data stat

L a L a L aR R R= + (5)

The first contribution, 2dataL aR , can be computed as:

22 2 2

1 2 2

1rtxN data

data i L bL a L b L a data

i L b L b

BR R PB H=

⎡ ⎤= ⋅ + ⋅⎢ ⎥ −⎣ ⎦

∑ (6)

where the term between brackets represents the average number of (re)transmissions per L2b-PDU, and the last term correspond to the RLC overhead.

The second contribution, , represents the throughput generated by status report requests. Such requests are sent whenever no answer to the last L2b-PDU of a cycle is received. For simplicity, status reports are assumed to arrive always correctly; however, when the last L2b-PDU of a cycle (carrying an implicit report request) is lost, the transmitter must send a new report request. Concretely, this contribution is given by:

22 2 22 / 2

2

statcyclesstat L b

L a L b L aL b L c datartx L b

N BR R PN B

= ⋅ ⋅ ⋅ (7)

where Ncycles represent the required mean number of retransmission cycles to send one L2c-PDU; represents the mean number of L2b-PDUs (including retransmissions) per each L2c-PDU; and represents the mean size of a data and control L2b-PDU, respectively.

Let ni be the number of (re)transmitted L2b-PDUs in cycle i. Then, the average number of L2b-PDUs per L2c-PDU can be computed by:

{ }( )2 / 20 1

Prrtx rN N

L b L c ii j

N j n j= =

= ⋅ =∑∑ (8)

where Pr{ni = j} is the probability of sending j L2b-PDUs in cycle i, given by the following recursion:

{ } { }1Pr Pr ( , )rN

i ik j

n j n k B k j−=

= = = ⋅∑ ( ) 2 2being , (1 )j k jL a L a

kB k j P P

j−⎛ ⎞

= ⋅ ⋅ −⎜ ⎟⎝ ⎠

(9)

2statL aR

2 / 2L b L cN2

dataL bB 2

statL bB


Solving the recursion:

{ } { } ( )1 2 1 1

1

0 10

Pr ... Pr ,r r r

i i i m m

N N N i

i r rn j n n n n r

n j n j B n n− − − +

−

+= = = =

⎛ ⎞⎛ ⎞= = = ⋅⎜ ⎟⎜ ⎟

⎝ ⎠⎝ ⎠∑ ∑ ∑ ∏

(10)

The required mean number of retransmission cycles to send one L2c-PDU can be expressed by:

rtxN

cycles ii 0

N P=

= ∑ (11)

where Pi is the probability of requiring the ith cycle to successfully complete the transmission, whose value is computed as follows. Let be the probability that one L2c-PDU requires k, and only k, retransmission cycles. If Pr{n0} represents the probability that one L2c-PDU is segmented into n0 L2b-PDUs, then is expressed as:

( ) { } ( ) { }

( ) ( ) { }

( ) ( ) ( ) { }

0

0

0 0

0

1

0 1

0 1

0 1

0 0 2 01 1

0 1 1 01 1

0 1 1 01 1 1

, 0 Pr 1 Pr

, , 0 Pr

... , ... , , 0 Pr

r r

r

kr

kk

N Nn

c L an n

nN

cn n

n nN

c k k kn n n

P B n n P n

P B n n B n n

P B n n B n n B n n−

= =

= =

−= = =

= ⋅ = − ⋅

= ⋅ ⋅

= ⋅ ⋅ ⋅ ⋅

∑ ∑

∑∑

∑∑ ∑

…

(12)

Simplifying the expression for kcP , then Pi can be computed by:

{ } ( ) ( )( )0 0

0

1 11

0 2 20 0 1

1 1 Pr 1 1r

k

Ni i n nk ki c L a L a

k k nP P n P P

− −+

= = =

⎡ ⎤⎡ ⎤= − = − ⋅ − − −⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦∑ ∑ ∑

(13)

From previous equations, RLC throughput is given by:

22

22 22

1 2 2 2 / 2 2

1rtx

L aL b N data stat

L a cyclesi L b L bL a data data

i L b L b L b L c L b

RRP NB Bi P

B H N B=

=⋅⎛ ⎞

+ ⋅ ⋅ + ⋅⎜ ⎟ −⎝ ⎠∑

(14)

4.2.3 RLC Delay

To compute the mean L2b-PDU delay, DL2b, we need to analyze the impact of retransmissions, which depend on the loss rate at the layer below, PL2a. In particular, the additional delay introduced by retransmissions at each cycle comes from: a) delay to retransmit ni L2b-PDUs, noted as ; and b) delay to correctly receive the status report from the receiver, noted as Tstat_ok. These factors are combined as follows:

kcP

kcP

inD


( )2 2 2 _1

rtx

i

Ni

L b L a L a n stat oki

D D P D T=

= + ⋅ +∑ (15)

Tstat_ok includes the average delay of receiving the status request from the receiver (Tstat) and the additional delay introduced when the last L2b-PDU of a cycle is lost (i.e. timer expires and a new retransmission is triggered):

( )_ 2 2 _stat ok L a L a w stat statT P D T T= ⋅ + + (16)

Let Nr be the maximum number L2b-PDUs in which an L2c-PDU is segmented. can be computed from the probability of sending j L2b-PDUs at each cycle (Pr{ni = j}, see Eq. (10)):

{ }( )21

Prr

i

N

n i L aj

D n j D=

= = ⋅∑ (17)

The complete RLC model is summarized as follows:

RLC Model

12 2

rtxNL b L aP P +=

{ }

1

22 0 2

2 2 21 2 2 2

0 1

1Pr

rtx

rtx

rtx r

N

N data L a i stati L b i L b

L b L a L a N Ndata datai L b L b L b

ii j

P PB BR R i P

B H Bj n j

−

=

=

= =

⎡ ⎤⋅⎢ ⎥⎛ ⎞⎢ ⎥= ⋅ + ⋅ ⋅ + ⋅⎜ ⎟⎢ ⎥−⎝ ⎠ ⋅ =⎢ ⎥

⎣ ⎦

∑∑

∑∑

{ }( ) ( )2 2 2 2 2 2 _1 1

Prrtx rN N

iL b L a L a i L a L a L a w stat stat

i jD D P n j D P D T T

= =

⎡ ⎤= + ⋅ = ⋅ + ⋅ + +⎢ ⎥

⎣ ⎦∑ ∑

(18)

4.3. PDCP Layer

PDCP layer is in charge of adapting the data to achieve an efficient transportation through the radio interface. This layer performs header compression, which reduces network and transport headers (e.g. TCP/IP or RTP/UDP/IP). The most advanced header compression technique is known as RObust Header Compression (ROHC) [RFC 3095], which has been adopted by cellular standardization bodies like 3GPP. Using ROHC, the RTP/UDP/IPv4 header is compressed from 40 bytes to approximately 1 to 4 bytes.


4.3.1 PDCP Loss Rate

L2c-PDU loss rate, PL2c, comes from erroneous L2c-PDUs ( ), and L2c-PDUs discards at PDCP queues ( 2

overL cP )4:

2 2 2 2 2 2 2error over error over error over

L c L c L c L c L c L c L cP P P P P P P= + − ⋅ ≈ + (19)

Taking into account that an L2c-PDU is correctly transmitted if the Nr L2b-PDUs (in which it was segmented) arrive correctly at the receiver, is computed as the probability of requiring at least Nrtx + 1 retransmissions, Pi (see Eq. (12)):

{ } ( ) ( )( )12 0 2 21

0 11 Pr 1 1

rtx r

rtx

N N j jerror k kL c i L a L ai N

k jP P n j P P+

= += =

⎡ ⎤⎡ ⎤= = − = ⋅ − − −⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦∑ ∑

(20)

4.3.2 PDCP Throughput

The computation of PDCP throughput, RL2c, must take into account the lower layer throughput as well as the effect of ROHC. Assuming an average ROHC compression gain Gc, RL2c is given by the following expression:

( ) ( )12 3 4

2 22 2

1L c L L cL c L b

L c L b

B H H GR R

B H

−+ + ⋅ −= ⋅

+ (21)

4.3.3 PDCP Delay

L2c-PDU delay has been defined as the time elapsed between that PDU arrives (from upper layers) to the PDCP sublayer at the transmitter until an acknowledgement is received from the receiver. Hence, the average delay at PDCP layer, DL2c, comprises the time to correctly receive all L2b-PDUs in which an L2c-PDU is segmented; such delay includes potential L2b-PDU retransmissions, up to a maximum of Nrtx. Each retransmission cycle i adds two delay contributions: a) delay to (re)transmit ni L2b-PDUs ( ), defined in Eq. (17), and b) delay to receive the status report from the receiver (Tstat_ok), defined in Eq. (16):

( )2 _0

rtx

i

N

L c n i stat oki

D D P T=

= + ⋅∑ (22)

being Pi the probability of requiring i (re)transmission cycles, defined in Eq. (13)

4 In the access node, there is one dedicated PDCP buffer for each connection, whose size is QL2c. The term

2over

L cP is determined by the buffer size and the incoming traffic load.

2error

L cP

2error

L cP

inD


Finally, DL2c can be expressed by:

{ }( ) ( )( )2 2 2 2 _0 1

Prrtx rN N

L c i L a i L a L a w stat stati j

D n j D P P D T T= =

⎡ ⎤= = ⋅ + ⋅ ⋅ + +⎢ ⎥

⎣ ⎦∑ ∑ (23)

The complete PDCP model is summarized as follows:

PDCP Model { } ( ) ( )( )1

2 0 2 2 20 1

1 Pr 1 1rtx rN N j jk k over

L c L a L a L ck j

P n j P P P+

= =

⎡ ⎤⎡ ⎤≈ − = ⋅ − − − +⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦∑ ∑

( ) ( )12 3 4

2 22 2

1L c L L cL c L b

L c L b

B H H GR R

B H

−+ + ⋅ −= ⋅

+

{ }( )2 2 2 _0 1

Pr ( ( ) )rtx r

j

N N

L c i a i L a L a w stat stati j


⎡ ⎤= = ⋅ + ⋅ ⋅ + +⎢ ⎥

⎣ ⎦∑ ∑

(24)

5. NETWORK, TRANSPORT AND APPLICATION LAYERS MODEL

5.1. Network Layer

The average delay, throughput and loss rate at the IP layer is analyzed in this section. IP links have been assumed to be over-dimensioned compared to radio links. The well-known Weighted Fair Queuing (WFQ) multiplexing algorithm has been evaluated in the IP routers by means of simulations.

End-to-end IP performance has been analyzed from the performance results obtained at IP-fixed and radio domains, as shown in Figure 1. The following considerations have been made:

1. L3-PDU loss rate can be computed as the aggregation of the L3-PDU losses occurred in each domain: radio (PL3') and fixed (PL3'').

2. Mean throughput achieved by the mobile terminal is given by the most limiting point in the network, i.e. radio interface (RL3').

3. Mean end-to-end IP delay can be computed as the aggregation of the delays experienced in each domain: radio (DL3') and fixed (DL3'').

Considering previous statements, quality indicators at network layer are given by:

IP Model

3 3 3 2 3' '' ''L L L L c LP P P P P= + = +

( ) ( )3 3 3 3 2 3 3 2min ', '' 'L L L L L c L L L cR R R R R B B H= = = ⋅ +

3 3 3 2 3' '' ''L L L L c LD D D D D= + ≈ + (25)

5. Network, Transport and Application Layers Model

5.2. Transport Layer

This section aims at analyzing how lower layers affect the performance of UDP, TCP and TFRC-based applications.

• UDP

Since UDP does not include any congestion control or retransmission mechanism, UDP throughput can be simply computed from IP throughput and considering the header overhead. Assuming that the bottleneck of the end-to-end connection is located in the radio interface, the achieved performance for UDP protocol is given by

UDP Model

4 3L LP P=

( )4 3 4 4 3L L L L LR R B B H≈ ⋅ +

4 3L LD D≈

(26)

Note that both the delay and error rate at transport layer are similar to the lower layer results, assuming that processing delays are negligible.

• TCP

TCP define a congestion control mechanism to react against network congestion. When TCP is used as transport protocol, application throughput behavior depends on the specific TCP implementation. An analytic characterization of the steady state throughput for TCP-Reno protocol [Padhye, 1998] has been applied in this work. This model characterizes TCP throughput as a function of loss rate in the network PL3, Round-Trip-Time (RTT), Retransmission Time-Out duration (T0), maximum TCP window size (W) for a bulk transfer TCP flow, and the number of packets (b) acknowledged by each received ACK. Note that RTT value can be approximated by the mean two-way delay along the end-to-end network: RTT ≈ 2·DL3. The complete characterization of the TCP source rate, assuming that the maximum TCP window size has been reached, is computed in [Padhye, 1998].

Let us briefly analyze how network conditions affect the behavior of these transport protocols. Firstly, TCP performance is highly sensitive to packet losses because of their inherent congestion control mechanism, which decreases the window transmission even though such losses are not due to congestion. Secondly, the higher the RTT the lower the throughput at transport layer, because the congestion window depends on RTT rate. Finally, the reduction of the source throughput (caused by the congestion control mechanism) leads to a decrease of the overall load in the network, thus alleviating the loss rate and RTT.

A proper congestion window setting, in addition to adequate queue dimensioning along the network elements, is a key aspect for optimizing the end-to-end performance. In particular, the maximum window size is suggested to be slightly higher than the


Bandwidth-Delay Product (BDP) in order to exploit the available radio capacity [Gómez, 2005]. Consequently, a maximum TCP window size of W = 16 kbytes has been selected for the analysis. Since the queue sizes (per user) are higher than W value, we may assume that the probability of overflow in the queues is negligible, i.e. ≈ 0 and PL3''≈ 0. Hence, the contribution to the L4-PDU loss rate only comes from lost L2c-PDUs at the radio interface.

In steady state, TCP source rate, i.e. incoming rate to L3 (SL3), can be characterized by [Padhye, 1998]:

( )

( )( )L3

0

1 p 1W Q Wp 1 pS

b 1 p f pRTT W 2 Q W T8 pW 1 p

−+ +

−=⎛ ⎞−

+ + + ⋅ ⋅⎜ ⎟ −⎝ ⎠

(27)

being

( )( ) ( ) ( )( )( )( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛

−−−−−+−−

=−

W

W

ppppWQ

11111111,1min)(

333

(28)

65432 32168421)( pppppppf ++++++= (29)

where p represents the loss rate in the network PL3, T0 is the retransmission time-out duration, b represents the number of packets acknowledged by a received ACK, and RTT is the Round-Trip-Time value.

From equations (27), the following dependence is clearly identified: SL3 =Φ (DL3,PL3) where Φ represents the TCP or TFRC throughput equation (27). In addition, average delay DL3 and loss rate PL3 in the network depends on the total network load, SL3·Nu , e.g. high load in the network lead to higher delays and losses. Hence, source rate SL3 can be computed by solving the following system of equations:

SL3 =Φ (DL3,PL3) DL3 = f1(SL3·Nu) PL3 = f2(SL3·Nu)

(30)

which can be expressed by the following non-linear equation:

( )3 1 3 2 3 0f ( · ), f ( · ); , ,L L u L uS S N S N W b T= Φ (31)

To solve this non-linear equation, the behavior of DL3 = f1(SL3·Nu) has been parameterized using standard curve fitting methods from the result of Eq. (24)-(25). The second dependence, f2(SL3·Nu), can be also computed from Eq. (24)-(25), but considering, in this case, ≈ 0 and PL3' ≈ 0 (as stated before).

2over

L cP

2over

L cP

5. Network, Transport and Application Layers Model

Regarding TCP delay, it depends on the probability of retransmissions and the period of time Drtx required by the transmitter to detect the need of a retransmission (via duplicated ACKs or timer expiration). As stated by [Padhye, 1998], such time period Drtx can be computed by:

2

02 2 (1 ) 2 ( )1 ( )

6 3 6 1rtxb b p b f pD RTT Q W T

p p

⎛ ⎞+ − +⎛ ⎞⎜ ⎟≈ ⋅ + + + + ⋅ ⋅⎜ ⎟⎜ ⎟ −⎝ ⎠⎝ ⎠ (32)

where Q(W) and f(p) where defined in (28) and (29), respectively.

Finally, TCP delay, DL4, can be computed from the IP level delay by adding the effect of TCP retransmissions:

( )4 3 30

iL L rtx L

iD D p D D

∞

=

≈ + ⋅ +∑ (33)

where p represents the loss rate in the network, PL3.

Once SL3 is obtained by solving the aforementioned non-linear equation, the achieved TCP performance is given by:

TCP Model 4 0LP =

( ) ( )4 3 3 3 1 3 2 3 01 , f ( · ), f ( · ); , ,L L L L L u L uR S P being S S N S N W b T≈ ⋅ − = Φ

( ) ( )4 3 3 30

iL L L rtx L

iD D P D D

∞

=

≈ + ⋅ +∑

(34)

Note that the transport layer becomes error-free (PL4 = 0) since TCP is a reliable protocol.

• TFRC

TFRC has a lower throughput variation over time compared with TCP, which, in principle, makes it more suitable for real-time applications such as telephony or streaming media where a relatively smooth sending rate is important. The recommended TFRC throughput equation described in [Floyd, 2003] has been used, which is a simplified version of the throughput equation for Reno TCP when PL4 < 0.54 and no delayed-ACK is applied, i.e. b = 1. TFRC source throughput can be computed by [Xu, 2006]:

( )1

2 3 1 323 8

L32

Sp pRTT RTO 3 p p

=⋅ + ⋅ ⋅ ⋅ +

(35)

The evaluation of SL3 is fulfilled following the same procedure as in the TCP case, i.e. resolving the non-linear equation described in Eq. (31). The achieved TFRC performance is given by:


TFRC Model

4 2L L cP P≈

( ) ( )4 3 3 3 1 3 2 3 01 , f ( · ), f ( · ); , ,L L L L L u L uR S P being S S N S N W b T≈ ⋅ − = Φ

4 3L LD D≈ (36)

Since TFRC only includes the congestion control mechanism (and not retransmissions), remaining losses at transport layer for TFRC come from non-corrected errors at the radio link, and TFRC delay is similar to the network delay (DL3).

5.3. Application Layer

Application layer is responsible for establishing the streaming session, and afterwards, transfer the coded multimedia content (at the server side) and reproduce that content at the client side.

At the streaming server, application data is delivered to the transport layer at an average rate defined by the codec (see Table 2). However, if the transport layer includes a congestion control mechanism (e.g. TCP or TFRC), the socket between these layers must temporarily queue the packets when the transport layer rate is lower than the codec rate. This effect has been approximated by an M/M/1/L system queue wherein the arrival rate is given by λ=SL4/BL4 and the service rate is given by µ=SL3/BL3. L represents the socket queue size, which is assumed to be 64 kbytes. Loss rate in an M/M/1/L queue is given by:

( )1

11

L

socket LP beingρ ρ λρ

ρ µ+

−= =

− (37)

whereas the average waiting time in the socket can be obtained from:

( )( ) ( )( )( )( )

1 1

1

1 1 1 11 1

L L L

socket L

L LD

ρ ρ ρ ρ ρ ρ

µλ ρ ρ

+ +

+

− + + − − −= +

− − (38)

At the receiver side, the application layer adds an additional delay introduced by the application buffer of the streaming player. A big enough application buffer size that masks the network jitter to the application performance has been assumed. Then, considering that application throughput is not interrupted due to buffer starvation, the following expressions can be applied at application layer:

Application Model 5 4L L socketP P P= +

( )5 4 5 5 4L L L L LR R B B H= ⋅ +

5 4L L Buffer socketD D D D≈ + +

(39)

where Psocket and Dsocket are only applicable to TCP or TFRC-based applications.

6. End-to-End Model Results 201

From the end user perspective, the delay introduced by the application buffer, DBuffer, can be considered as part of the session establishment since the application does not start reproducing media until the buffer is full. Buffer duration in seconds usually spans from 1 to 10 (depending on the technology), in order to optimize the service continuity. However, in two-way streaming services (like Push-to-Talk over Cellular, PoC) the former is generally small (not higher than 500 ms) since the interactivity requirements are much stricter than they are in one-way streaming services.

6. END-TO-END MODEL RESULTS

This section presents some results obtained from the proposed model.

6.1 UDP-based solution

Starting with the UDP-based solution, average throughput at different layers is shown in Figure 4 as a function of the total application load, SL5 ·Nu. For simplicity, only two different multiplexing algorithms are shown (RR and M-LWDF). As aforementioned, mean source rate per user has been kept constant (SL5 = 384 kbps) while the number of users Nu in the system was increased. Figures on the left show throughput results without ROHC whereas figures on the right include the header compression feature.

b) With ROHC

0 2 4 6 8 10 12 14 16 18 20

150

200

250

300

350

400

450

500

Thro

ughput

at L

ayer

i, R

Li (K

bps)

a) Without ROHC

RL2a

RL2b

RL2c

RL3

RL4

RL5

0 2 4 6 8 10 12 14 16 18 20

150

200

250

300

350

400

450

500

RL2a

RL2b

RL2c

RL3

RL4

RL5

Application level load, SL5·Nu (Mbps)

Th

roug

hput

at L

ayer

i, R

Li (

Kbps)

100100

0 2 4 6 8 10 12 14 16 18 20

150

200

250

300

350

400

450

500

Thro

ughput

at L

ayer

i, R

Li (K

bps)

a) Without ROHC

RL2a

RL2b

RL2c

RL3

RL4

RL5

100

RR RR

M-LWDF

b) With ROHC

0 2 4 6 8 10 12 14 16 18 20

150

200

250

300

350

400

450

500

RL2a

RL2b

RL2c

RL3

RL4

RL5

Thro

ughput

at L

ayer

i, R

Li (

Kbps)

100

M-LWDF


Application level load, SL5·Nu (Mbps) Application level load, SL5·Nu (Mbps)

Figure 4. Throughput results for UDP-based streaming


Analyzing the performance shown in Figure 4, the following QoS degradation effects can be pointed out at layer i:

L2a) MAC layer throughput, RL2a, is rapidly degraded above a certain critical load point, which corresponds to the maximum achievable system throughput for a particular multiplexing algorithm. M-LWDF algorithm achieves higher system throughput (about 12 Mbps with scenario settings) than RR since M-LWDF takes Channel State Information (CSI) into account, thus providing higher diversity gain [Entrambasaguas, 2007].

L2b) RLC layer introduces an additional performance degradation due to L2b-PDU retransmissions, as established in equation (18). For UDP-based solution, the maximum number of retransmissions Nrtx is set to 3, which provides reasonable reliability results for the specific settings.

L2c) The use of ROHC allows decreasing the required amount of resources below PDCP layer while achieving the same application level throughput. Due to compression, PDCP layer may even compute a higher throughput (after decompression) than lower layers, as illustrated in the figures on the right.

L3-L5) Throughput at upper layers only suffers from RTP/UDP/IP header overheads.

Throughput curves in Figure 4 also provide very valuable information about the required resources at each layer in order to fulfill the desired QoS at the application level. For instance, once our streaming application negotiates the bitrate of the codec (384 kbps), the proposed model is able to map such requirement at any layer below, e.g. to perform resource reservation of ≈400 kbps at IP level.

6.2 TCP / TFRC-based solutions

For TCP and TFRC, the following parameter settings have been used for the congestion control mechanisms: W = 32 kbytes, b = 2 packets for TCP and b = 1 packet for TFRC, and T0 = 4·RTT, with RTT ≈ 2·DL3. The optimum maximum number of RLC retransmissions Nrtx will depend on the loss rate at MAC layer (PL2a).

The maximum achievable TCP throughput and delay as a function of the loss rate at MAC layer (PL2a) and Nrtx is shown in Figure 5. Results are shown for two load conditions (Nu = 5 users and Nu = 45 users).

Regarding TCP throughput results, shown in Figure 5 a) and b), it is shown how high PL2a values require a higher number of RLC retransmissions to minimize data losses, and consequently, maximize the throughput. For low load conditions (Nu = 5 users), potential TCP throughput is higher than the video codec rate (384 kbps) as long as a proper Nrtx

value is configured. However, for high load conditions (Nu = 45 users), TCP is not able to achieved the desired throughput.


Concerning TCP delay results, shown in Figure 5 c) and d), two different scenarios are analyzed:

a) Low load (Nu = 5): in general, high loss rates at MAC sublayer (PL2a) must be reduced by RLC retransmissions (configuring a high value of Nrtx parameter). As radio interface delay is very low in low load conditions, the impact of RLC retransmissions on TCP delay is almost negligible. Otherwise, if Nrtx is set to a low value, TCP will be responsible for performing end-to-end retransmissions, thus increasing L4-PDUs delay.

b) High load (Nu = 45): besides the previous effect, high load conditions increase the radio interface delay, thus consecutive RLC retransmissions will increase the end-to-end RTT. As TCP delay depends on average RTT, high Nrtx will lead to high TCP delays. Besides, as TCP throughput (per user) increases for high Nrtx values, the overall load in the network is higher, thus contributing to the TCP delay.

02

46

81010-3

PL2a

10-2

10-1

100

100

101

102

103

104

105

106

10-3

02

46

810

PL2a

10-2

10-1

100

100

101

102

103

104

105

106

107

Nrtx

Potential TCP throughput (bps)

Nrtx

0.0310.031

384kbps

384kbps


02

46

810D

elay of L4-PDUs, DL4 (s)

101

102

10-1

100

Nrtx PL2a

10-310-2

10-1100

High RTT

0.031

02

46

810

101

102

103

10-1

100

Nrtx

0.031

PL2a10-3

10-2

10-1100

10-2

Delay of L4-PDUs, DL4 (s)

High loss rateHigh loss rate

a) TCP Throughput, Nu = 5 users b) TCP Throughput, Nu = 45 users

c) TCP delay, Nu = 5 users d) TCP delay, Nu = 45 users Figure 5. Effect of the number of retransmissions on TCP throughput and delay


According to the results shown in Figure 5, for a given PL2a, there is an optimum Nrtx value that maximizes the throughput while keeping the delay as low as possible. For instance, for PL2a = 0.031 (obtained from a BERT = 10-4 at the physical layer), the optimum value of Nrtx is 6. This value will depend on the loss rate, e.g. for PL2a = 0.062 (BERT = 2·10-4), the value of Nrtx that optimizes the transport layer performance is 8. In that sense, the proposed model is very useful for cross-layer optimization purposes.

The behavior of TFRC protocol is similar (results not shown in this extended abstract), though with the following differences. Potential TFRC throughput for high load conditions (Nu = 45 users) is higher than codec rate if Nrtx is set properly. Additionally, since TFRC does not perform end-to-end retransmissions, TFRC delay is not affected by this factor when PL2a is high.

Figure 6 shows the TCP throughput and delay results for different loss rate (PL2a = 0.031 y PL2a = 0.062) and load conditions (Nu = 5 y Nu = 45 users). For PL2a = 0.031, the minimum value of Nrtx that allows achieving the maximum potential throughput is Nrtx = 6, independently of the number of users in the system, as shown in Figure 6 a) and b). This minimum value of Nrtx is selected in order to minimize the end-to-end delay. However, for PL2a = 0.062, a higher value of Nrtx is required to achieve the maximum potential throughput, as shown in Figure 6 c) and d).

1 2 3 4 5 6 7 8 9 109

10

11

12

13

14

15

RL4

DL4

Delay of L4-PDUs, DL4

(ms)

102

101

103

104

105

106

107

Nrtx

0

500

1000

1500

2000

2500

3000

3500

4000

102

101

103

104

105

106

107

1 2 3 4 5 6 7 8 9 10Nrtx

RL4

DL4

1 2 3 4 5 6 7 8 9 105

10

15

20

25

30

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Potential TCP throughput

RL4

DL4

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384 kbps

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(ms)



(ms)



(ms)


Figure 6. Potential throughput vs. delay at transport layer (TCP)


Figure 7 shows the impact of the BERT on the throughput and delay at the transport layer. As the BERT is lower, the potential TCP throughput achieves its maximum value for a lower Nrtx value, since the physical layer provides higher reliability. However, the impact of a lower BERT on the TCP delay depends on the load level, leading to lower TCP delays for low load and higher TCP delays for high load.

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(ms)


(ms)

Figure 7. Impact of the BERT on TCP throughput and delay

The impact of the maximum TCP window size (W) on TCP throughput and delay is shown in Figure 8. Performance results show that too small values of the maximum congestion window (W) do not allow to fully utilize the resources, thus reducing the maximum throughput. On the other side, too large values of W requires a high reliability (in terms of loss rate) to use the whole window; thus, too many RLC retransmissions are required, which increases the end-to-end delay.


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(ms)

Higher W

Figure 8. Impact of maximum TCP window size (W) on TCP throughput and delay

(BERT = 10-4 and Nu = 45 users)

The values of BERT, Nrtx and W parameters must be jointly decided, having a trade off between throughput and delay. For a given BERT = 10-4, Nrtx has been set to 6 in order to limit the end-to-end delay. With these values of BERT and Nrtx, the maximum TCP window that maximizes the throughput is W = 32 kB. Another possibility to avoid performing too many retransmissions is to decrease the BERT so that loss rate at RLC layer is lower. That way, Nrtx can be decreased at the expense of degrading the throughput.

A performance comparison among UDP, TCP and TFRC performance is shown in Figure 9. Throughput results shown in Figure 6 (left) correspond to the application layer throughput (RL5) as a function of the overall load at application layer (x axis) by increasing the number of users Nu in the system. The figure also shows the maximum potential throughput per user (plotted in dashed lines), which is limited by the congestion control algorithm. While a non real-time service (FTP-like) could transmit at this rate, real-time applications are limited by the codec bitrate (an average 384 kbps in our analysis).

Note that above the IP congestion point, a particular total throughput represents a different number of users for different transport protocols as the source rate is differently adapted to network conditions. Although this is the common way of representation, it is also interesting to represent the same throughput results as a function of the number of users (as shown in Figure 9-right). In this case, all transport protocols achieve similar throughput results. Above the critical load point, UDP continues sending at the average codec rate, producing a high loss rate due to overflow in the queues. On the other hand, both TCP and TFRC decrease their sending rates as queue occupancy grows (to react against congestion). In that situation, application data is temporarily stored in a large buffer at the streaming server. The application is aware of buffer occupancy and, consequently, may carry out corrective actions: a) decrease the codec rate, b) drop packets, or c) close the application. This intrinsic advantage of protocols with congestion control

7. Model Validation 207

mechanisms (TCP and TFRC) versus UDP may be a decisive factor to select the transport protocol.

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TFRCTCP

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IP congestion

(WFQ)

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(M-LWDF)

20


Application Throughput, RL5 (Kbps)

Max. allowed by

congestion control

Application Throughput, RL5 (Kbps)

Figure 9. Throughput comparison for different transport protocols

7. MODEL VALIDATION

The objective of this section is to validate the theoretical model proposed along this work. The validation process has been divided into two phases: 1) validation of the radio interface model, and 2) validation of upper layers model.

7.1. Radio Interface Model Validation

Since the radio technology under study is not available yet, the validation process of the radio subsystem is based on link level simulations.

Figure 10 shows the validation results for the radio interface model5, assuming the M-LWDF multiplexing algorithm and Nrtx = 3. Performance estimations from the theoretical model, in terms of delay (figure on the left) and throughput (figure on the right), are compared with simulation results.

5 Since our QoS model is based on MAC layer simulations as a starting point, the goal of the radio interface model validation is to validate the results of the RLC and PDCP models.


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Figure 10. Radio Interface Model Validation

7.2. Upper Layers Model Validation

The validation process of upper layers (i.e. network, transport and application) has been performed by developing a real-time end-to-end system validation. Figure 11 shows the validation system architecture, which includes the following modules:

• Darwin Streaming Server v5.5.5 has been used at the server side. This server allows selecting UDP or TCP as transport protocol. Streaming content is based on a single video flow whose parameters are listed in Table 2. A packet sniffer (Wireshark v0.99.7) is used both at server and client sides to capture and analyze the traffic between peers.

• At the client side, VLC Media Player 0.8.6d is responsible for establishing the streaming session with the server and reproducing the content. For the TCP-based solution, TweakMaster v2.50 has been also used to align TCP settings at the client side with the parameters assumed in the theoretical model.

• Between the client and the server, a real-time emulator aims at modeling the behavior of the whole network, so that the client-server connection experiences (in real-time) the quality degradation introduced along the end-to-end path. This emulator uses the packet filtering framework included in the Linux 2.4.x and 2.6.x kernel series together with the iptables utility. Iptables allows configuring the packet filtering rule set. Certain quality degradation (in terms of delay or packet loss) is applied to the filtered packets. Such degradation is set according to the quality indicators obtained at IP layer: loss rate PL3 and delay DL3. That way, the emulator offers a real-time data flow that experiences the degradation introduced by the wireless access network.


RTSP

SDP

RTP / RTCP

TCP Transport Protocol

Audio/video

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IP IP

Streamingclient

Wireless Access Network

Streamingserver

L2/L1

PC-2 (Windows) PC-1 (Linux)

Forward

DiscardDelay

L3-PDUs

ForwardDelay

Real-time Emulator

Figure 11. Validation system architecture

Starting with the UDP-based solution, a comparison between application throughput obtained from the end-to-end model and measurement results is shown in Figure 12. In this case, the theoretical model is proved to provide very accurate estimations for any load value. In addition, a snapshot of the video captured at the client is shown for three different load levels in order to illustrate the image quality degradation as load increases.

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Figure 12. Application throughput validation (UDP-based solution)


Regarding the TCP results, Figure 13 shows the impact of the load in the TCP source rate. As a consequence of TCP congestion control mechanism, average TCP source rate at the server is highly influenced by network conditions (loss rate and delay), adapting the source rate to react against congestion. On the contrary, UDP delivers data to the network at a source rate determined by the application layer (and not by the network performance).

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Figure 13. Impact of network load on the source rate at the server

Theoretical delay estimations have been validated at transport layer (TCP) and application layer (RTP).

In order to validate TCP delay results, the received ACKs have been used to estimate the round trip time (RTT) of L4-PDUs, assuming DL4 = ½ RTT. These RTT measurements have been processed by tcptrace v6.6.0, from trace files captured with Wireshark. Real measurements of TCP delay have allowed validating theoretical results from the proposed QoS model (see Figure 14).

Validation of RTP delay is more complex as there is no feedback information from the receiver to measure the RTP RTT. The solution is based on using RTCP time stamp to measure the delay from sender to receiver; this solution requires sender and receiver to be synchronized via Network Time Protocol (NTP). Since a perfect synchronization via an external NTP server is not feasible, RTP delay validation provides slightly higher differences compared to TCP delay.


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Figure 14. TCP and RTP delay validation

Throughput results at application level obtained from the model and from real measurements are compared in Figure 15. A proper behavior of the theoretical model is observed. However, the proposed model provides less accurate values for high load conditions due to the following assumption taken during the TCP modeling: the retransmission timeout duration is assumed to be constant in the model: T0 = max(4*RTT, 1), whereas the T0 value in the real implementation is adaptively determined by estimating the mean and variance of the RTT [Jacobson, 1992], thus providing a slightly better performance in the real system.

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Figure 15. Application throughput validation (TCP-based solution)


A summary of the numerical values of the main parameters used along this work are shown in Table 2 (for the non-configurable parameters) and Table 3 (for the configurable parameters).

Table 2. Non-configurable parameters

Parameter Description Value APPLICATION LAYER

Nu Number of users 2-50 SL5 Mean source rate at application layer 384 kbps BL5 Packet size at application layer Truncated Pareto (α = 1.2)

Mean = 480 bytes, Max = 1200 bytes

TL5 Mean interarrival packet time Truncated Pareto (α = 1.2) Mean =10 ms, Max = 20 ms

HL5 RTP header length 12 bytes VR Video resolution QVGA, 320x240 pixels FR Frame rate 15 frames/sec - Video encoding format 3GPP (based on MPEG-4) L Socket capacity 64 kbytes

TRANSPORT LAYER HL4 Transport header length 12 bytes (UDP)

20 bytes (TCP) 16 bytes (TFRC)

B Number of acknowledged packets by a received ACK 2 (TCP), 1 (TFRC) T0 Retransmission Time-Out max(4*RTT, 1)

IP LAYER Nn Number of IP nodes from server to client 3 HL3 IP header length (version 4) 20 bytes CL3 Minimum IP link capacity 20 Mbps QL3 IP queue size 64 kbytes

PDCP SUBLAYER HL2c Header length of L2c-PDU 1 byte Gc Compression gain achieved by ROHC 10 QL2c PDCP queues size 32 kbytes

RLC SUBLAYER HL2b Header length of L2b-PDUs 2 bytes Tstat Mean delay to receive a status report DL2a Tw_stat Timeout to retransmit new polling request 200 ms

2L b

statB Size of status report L2b-PDUs 4 bytes

2L b

dataB Size of data L2b-PDUs 40 bytes MAC SUBLAYER

HL2a Header length of L2a-PDUs 0 bytes BL2a Size of L2a-PDUs

2L b

dataB bytes PHYSICAL LAYER

TTI Transmission Time Interval 1 ms Nc Number of channels 50


ρc Correlation between consecutive channels 0.6 TB Frame duration TTI Mc Number of QAM symbols multiplexed on a channel 12 MT Number of QAM symbols per TB 12 Gcod Channel Coding Gain 8 dB

RADIO CHANNEL fD Doppler spread 8 Hz γ Average Signal to Noise Ratio (SNR) 15 dB

Table 3. Configurable parameters

Parameter Description Value TRANSPORT LAYER

W Maximum TCP window size 32 kbytes IP LAYER

- Queue Management algorithm RR, WRR, LDF, WFQ RLC SUBLAYER

Nrtx Maximum number of RLC retransmissions 3-6 MAC SUBLAYER

- Multiplexing algorithm RR, PF, LDF, BC, LDF-BC, M-LWDF

PHYSICAL LAYER BERT Target Bit Error Rate (BER) 10-4

CONCLUSIONS AND FUTURE RESEARCH

1. CONCLUSIONS

In this Ph. D. thesis, a detailed analysis of streaming QoS over wireless access networks has been provided. As a result, this thesis proposes a semi-analytic QoS model that allows evaluating the end-to-end service performance. Concretely, the end-to-end performance has been characterized by the cumulative quality degradation along the different network elements and protocols. The proposed QoS model provides a set of performance indicators (throughput, delay and loss rate) in the different protocol layers of the streaming service architecture. That way, the model helps to evaluate the QoS needs at different layers as well as to understand the main factors impacting the application performance.

Compiling the conclusions from each chapter, the main contributions of this Ph. D. thesis are summarized below:

• At the physical layer, a basic characterization of the main functions and parameters is given from a performance viewpoint. At this layer, the proposed model includes a multichannel and multiuser radio subsystem that uses adaptive modulation and channel coding. As a numerical example, the OFDM-based 3GPP-LTE cellular technology has been assumed at the physical layer. By means of simulations, the key performance indicators of the physical layer have been obtained. These results might be extrapolated to any other type of orthogonal multiplexing like TDM, CDM or SDM, by adjusting appropriately the channels and time correlation parameters.

216 Conclusions and Future Research

• A set of multiplexing strategies over a multichannel and multiuser environment have been studied at the MAC sublayer. Throughput, delay, jitter and loss rate results are evaluated for each multiplexing algorithm. It has been proved (analytically and from simulations) that those algorithms whose decision takes into account the instantaneous channel state are able to provide certain diversity gain. However, the best performance is achieved when both channel conditions and QoS indicators are considered in the decision. In that sense, M-LWDF algorithm outperforms the others in terms of fairness and capacity (obtaining a 50% higher capacity than Round Robin when channels are uncorrelated).

• At RLC sublayer, an analytic model of ARQ-based retransmission mechanism is proposed. The use of RLC retransmissions allows to reduce the link level error rate at the expense of decreasing the effective throughput and increasing the average delay. Hence, the maximum number of retransmissions (Nrtx) is a key parameter to optimize the overall performance. An optimum value of Nrtx parameter has been found for our specific scenario, being Nrtx = 6 (for TCP and TFRC-based solutions) and Nrtx = 3 (for UDP-based solution) the values that optimizes the performance of upper layers (for a given BERT = 10-4).

• PDCP sublayer has been analytically modeled by characterizing the segmentation and reassembly, and well as the use of robust header compression (ROHC). ROHC technique is able to decrease the amount of resources to be transmitted to lower layers though keeping the same application level throughput. Thus, taking into account the header decompression at the receiver, PDCP effective throughput may be higher than RLC throughput. According to our model’s results, the use of ROHC achieves a throughput gain of 7% (assuming a mean packet size of 480 bytes at application layer) for a constant number of users, or equivalently, a capacity gain (in term of users) by keeping the same QoS. Such capacity gain will be higher as the packet size decreases.

• The streaming server is accessed through an IP fixed-wireless network. In the IP domain, different multiplexing algorithms (RR, LDF, WRR y WFQ) have been evaluated. All of them provide similar results as long as the traffic from different users is homogeneous (i.e. similar source rate and QoS requirements); otherwise, WFQ is the only technique that provides a fair treatment among users since source rate, delay and variable packet size requirements are taken into account. However, the impact of IP domain in the end-to-end performance is usually minor as the radio link is assumed to be the bottleneck of the network.

• Different transport protocols have been modeled (UDP, TCP and TFRC) and evaluated, obtaining the following results:

When no congestion control is used in the end-to-end connection (i.e. UDP-based solution), high loss rate is achieved for high load due to buffers overflow.

1. Conclusions 217

When congestion control is used in the end-to-end connection (i.e. TCP and TFRC-based solutions), the loss rate at network level must be minimized. In that sense, RLC retransmissions play an important role to optimize the transport layer performance.

TCP is an appropriate transport protocol for streaming services as long as network conditions (in terms of delay and packet loss) are acceptable. Otherwise, el TCP behavior is highly influenced by these factors, especially by packet loss.

End-to-end TCP delay is very influenced by the loss rate in lower layers, as TCP reacts to congestion by reducing the transmission window and performing end-to-end retransmissions. In order to minimize the loss rate coming from the radio interface, a higher number of RLC retransmissions could be performed. Although RLC retransmissions lead to a higher delay in the radio subsystem, there is a range of Nrtx values that reduces the end-to-end delay.

As the BERT is lower, the potential TCP throughput achieves its maximum value for a lower Nrtx value, since the physical layer provides higher reliability. However, the impact of a lower BERT on the TCP delay depends on the load level, leading to lower TCP delays for low load and higher TCP delays for high load.

Too small values of the maximum congestion window (W) do not allow to fully utilize the resources, thus reducing the maximum throughput. On the other side, too large values of W requires a high reliability (in terms of loss rate) to use the whole window; thus, too many RLC retransmissions are required, which increases the end-to-end delay.

The values of BERT, Nrtx and W parameters must be jointly decided, having a trade off between throughput and delay. For a given BERT = 10-

4, Nrtx has been set to 6 in order to limit the end-to-end delay. With these values of BERT and Nrtx, the maximum TCP window that maximizes the throughput is W = 32 kB.

TFRC improves TCP performance in terms of capacity although TFRC is not a reliable protocol. Nevertheless, TFRC loss rate may be kept under a desired target (admissible by the application) by adequately adjusting the radio parameters.

For low load conditions, the evaluated transport protocols provide a similar performance. However, for high load, TCP and TFRC decrease their sending rate in order to avoid network congestion.


• In order to validate the proposed QoS model, a real-time emulation platform has been developed. This platform takes simulation results of the radio interface as an input to emulate a real streaming service over a wireless access. Emulation results allow to validate the accuracy of our proposed model and, additionally, to experience the end-to-end service quality. According to the validation results, performance indicators obtained from the model compared to emulation results are in an acceptable range of accuracy. The proposed model provides worst performance estimations for TCP under high load conditions due to several assumptions taken during the modeling process (see chapter 6).

• In a nutshell, the proposed QoS model allows to obtain a clear vision of the main factors that degrade the application performance as well as the different QoS needs at the different layers.

Figure 1 shows a summary of the complete QoS model.

2. Future research 219

IP(L3)

Transport(L4)

Application(L5) 5 4L LP P=

55 4

5 4

LL L

L L

BR RB H

= ⋅+

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( )4 3 31L L LR S P≈ ⋅ −

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( )1

2 3 1 323 8

L32

Sp pRTT RTO 3 p p

=⋅ + ⋅ ⋅ ⋅ +

4 0LP =

( )4 3 31L L LR S P≈ ⋅ −

( )

( )( )L3

1 p 1W Q Wp 1 pS

b 1 p f pRTT W 2 Q W RTO8 pW 1 p

−+ +

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4 3L LP P=

44 3

4 3

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L L

BR RB H

≈ ⋅+

4 3L LD D≈

3 2 3 ''L L c LP P P= +

33 2

3 2

LL L c

L L c

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= ⋅+

3 2 3 ''L L c LD D D≈ +

UDP TCP TFRC

Fixed IP network simulated

MAC(L2a)

RLC(L2b)

{ }

1

22 0 2

2 2 21 2 2 2

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rtx

rtx

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PDCP (L2c)

( ) ( )12 3 4

2 22 2

1L c L L cL c L b

L c L b

B H H GR R

B H

−+ + ⋅ −= ⋅

+

( ) 2

2 1 1 L aBL a TP BLER BER= ≤ − −

[ ]2 ,1

Nc

L a i kk

R R n=

=∑2L aD

,,

[ ] if channel is assigned to user[ ]

0 otherwisei k

i k

r n k iR n

⎧= ⎨⎩

12 2

rtxNL b L aP P +=

{ }( ) ( )( )2 2 2 2 _0 1

Prrtx rN N

L c i L a i L a L a w stat stati j


⎡ ⎤= = ⋅ + ⋅ ⋅ + +⎢ ⎥

⎣ ⎦∑ ∑

{ } ( ) ( )( )12 0 2 2 2

0 1

1 Pr 1 1rtx rN N j jk k queues

L c L a L a L ck j

P n j P P P+

= =

⎡ ⎤⎡ ⎤≈ − = ⋅ − − − +⎢ ⎥⎢ ⎥⎣ ⎦⎣ ⎦∑ ∑

DL1 ≈ TTI

PL1 ≤ BERTPHY(L1)

Simulated

Analytic

1 ,1

cNiL i k

k

R r=

= ∑

UDP TCP / TFRC( )5 4 4 1

11

L

L L socket L LP P P Pρ ρ

ρ +

−= + = +

−5

5 45 4

LL L

L L

BR RB H

= ⋅+

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( )( )1 1

1

1 1 1 11 1

L L L

socket L

L LD

ρ ρ ρ ρ ρ ρ

µλ ρ ρ

+ +

+

− + + − − −= +

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4

3

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λρµ

= =

2

02 2 (1 ) 2 ( )1 ( )

6 3 6 1rtxb b p b f pD RTT QW T

p p

⎛ ⎞+ − +⎛ ⎞⎜ ⎟≈ ⋅ + + + + ⋅ ⋅⎜ ⎟⎜ ⎟ −⎝ ⎠⎝ ⎠

( ) ( )4 3 3 30

iL L L rtx L

iD D P D D

∞

=

≈ + ⋅ +∑

, ,[ ] ( [ ], , )i k i k T codm n f n BER Gγ=, ,[ ] [ ]i k c T i kr n M M m n= ⋅ ⋅

Figure 1. Summary of the end-to-end QoS model


2. FUTURE RESEARCH

The main goal of this Ph. D. thesis has been the definition of a methodology that helps to evaluate the performance of a particular data service over a wireless access network. This is an open research area, from which the following research lines might be highlighted:

• Firstly, this type of analysis allows identifying the main factors impacting on the final QoS, and furthermore, quantifying such impact. A possible research line is to build behavioral abstract models that are managed by means of policy rules in order to control the end-to-end QoS1. Such models must include mapping procedures from high level rules to network configurations to achieve the desired performance (in terms of throughput, delay and/or loss rate) [Gómez, 2002a][Gómez, 2002b].

• Some streaming architectures includes a coding rate adaptation feature at the server to adequate the network conditions. This feature requires certain feedback periodically from the receiver. The analysis and modeling of this enhanced streaming architecture is proposed.

• The proposed QoS model can be extended to any other data service, from non real-time services like web browsing of file transfer to more advanced services like Voice over IP (VoIP). Although most of the work shall be common to any service, each particular application requires a detailed analysis of the traffic pattern, protocol stack and parameters as well as the revision of all the assumptions taken during the modeling process.

• Another aspect to be studied is the effect of heterogeneous traffic with different QoS requirements. This research line includes the analysis of the multiplexing algorithms to provide an appropriate treatment to each data flow as well as the evaluation of streaming quality when other traffic types share the network resources.

• The radio interface modeled in this work is based on 3GPP-LTE technology, though the methodology is applicable to any other wireless technology (like GPRS, UMTS, WLAN, WiMax, etc.) by characterizing the protocols and functionalities in a proper way.

1 This research area is known as “QoS policy management”.

2. Future research 221

• Another aspect to be treated is related to the optimization of application buffer length so that a minimum delay is experience while avoiding buffer starvation.

• Along this work, it has been shown the negative impact that packet losses have on TCP and TFRC performance, independently of whether such losses are due to congestion or due to radio interface errors. Therefore, a detailed analysis of the packet loss differentiation will be very valuable in order to apply congestion control only when packet losses are due to congestion.

• Data losses in the network has been proved to have a very negative impact on TCP and TFRC performance, independently whether such losses are due to network congestion or radio interface errors. Therefore, it is interesting to investigate on how to differentiate data losses in both domains so that congestion control mechanisms are only applied when network congestion occurs.

• Many different QoS mechanisms have been defined for IP networks, most of them have been kept out of the scope of this work. Hence, it is proposed to include in the analysis other IP QoS mechanisms like Differentiated Services (DiffServ), Integrated Services (IntServ), Multi-Protocol Label Switching (MPLS) or any type queuing management like Random Early Detection (RED).

• A possibility to avoid congestion is to apply admission control based on network resources availability and application QoS requirements. The evaluation of the impact that this admission control has on the service performance is proposed for future research.

• Another possible research line is focused on mobility aspects of the wireless connection. The objective of this line would be to analyze the impact of terminal mobility (speed, handovers, etc.) in the service performance.

• An interesting research line could be focused on the analysis of the subjective video quality. In particular, the analysis could be based on the evaluation of the video distortion for different scenarios so that end user quality of experience can be estimated.

REFERENCIAS

[3GPP] 3GPP (3rd Generation Partnership Project), www.3gpp.org

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[RFC 1323] IETF RFC 1323, “TCP Extensions for High Performance,” May 1992

[RFC 1633] IETF RFC 1633, “Integrated Services in the Internet Architecture: an Overview,” June 1994

[RFC 2326] IETF RFC 2326, “Real Time Streaming Protocol (RTSP),” April 1998

[RFC 2327] IETF RFC 2327, “SDP: Session Description Protocol,” April 1998

[RFC 2475] IETF RFC 2475, “Architecture for Differentiated Services,” December 1998

[RFC 2507] IETF RFC 2507, “IP Header Compression,” February 1999

[RFC 2581] IETF RFC 2581, “TCP Congestion Control,” April 1999

[RFC 2757] IETF RFC 2757, “Long Thin Networks,” January 2000

[RFC 2988] IETF RFC 2988, “Computing TCP's Retransmission Timer,” November 2000

[RFC 3095] IETF RFC 3095, “Robust Header Compression (ROHC),” July 2001

[RFC 3320] IETF RFC 3320, “Signaling Compression (SigComp),” January 2003

[RFC 3448] IETF RFC 3448, “TCP Friendly Rate Control (TFRC): Protocol Specification,” January 2003

[RFC 3481] IETF RFC 3481, “TCP over Second (2.5G) and Third (3G) Generation Wireless Networks,” February 2003

[RFC 3550] IETF RFC 3550, “RTP: A Transport Protocol for Real-Time Applications,” July 2003

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