Title Details

Professor Markus Rupp, University of Technology Vienna, Austria
Markus Rupp is presently a full professor for Digital Signal Processing in Mobile Communications at the Technical University of Vienna. Rupp previously held a postdoctoral position at the University of Santa Barbara, California. He was associate editor of IEEE Transactions on Signal Processing from 2002-2005, is currently associate editor of JASP EURASIP Journal of Applied Signal Processing, JES EURASIP Journal on Embedded Systems, Research Letters in Signal Processing, Research Letters in Communications, and is elected AdCom member of EURASIP. Professor Rupp has authored and co-authored more than 250 papers and patents on adaptive filtering, wireless communications and rapid prototyping as well as automatic design methods.

List of Contributors xiii

About the Contributors xv

Foreword xix

Preface xxi

Acknowledgements xxv

List of Abbreviations xxvii

I Cellular Mobile Systems 1

1 Introduction to Radio and Core Networks of UMTS 5
Philipp Svoboda and Wolfgang Karner

1.1 UMTS Network Architecture 7

1.2 UTRAN Architecture 8

1.2.1 UTRAN Protocol Architecture 9

1.2.2 Physical Layer Data Processing in the UTRAN Radio Interface 13

1.3 UMTSPS-core Network Architecture 16

1.4 A Data Session in a 3GNetwork 18

1.4.1 The UMTS (PS-core) Protocol Stack 19

1.4.2 The Protocols 20

1.4.3 Bearer Speed in UMTS 23

1.5 Differences between 2.5G and 3G Core Network Entities 23

1.5.1 GPRS Channels 24

1.5.2 GPRS Core Network Architecture 25

1.5.3 The GPRS Protocol Stack 25

1.5.4 Bearer Speed in GPRS and EDGE 27

1.6 HSDPA: an Evolutionary Step 27

1.6.1 Architecture of HSDPA 28

1.6.2 Difference between UMTS and HSDPA 29

1.6.3 Transport and Control Channels 31

References 32

II Analysis and Modelling of the Wireless Link 35

2 Measurement-based Analysis of UMTS Link Characteristics 39
Wolfgang Karner

2.1 Measurement Setup 40

2.1.1 General Setup 40

2.1.2 Mobility Scenarios 42

2.2 Link Error Analysis 46

2.2.1 Link Error Probability 46

2.2.2 Number of erroneous TBs in TTIs 48

2.2.3 TTI-burstlength,TTI-gaplength 48

2.2.4 TB Error Bursts, TB Error Clusters 50

2.2.5 The Influence of TPC on Link Error Characteristics 52

2.2.6 Statistical Dependency between Successive Gaps/Bursts 54

2.2.7 Block Error Ratio (BLER) 55

2.3 Dynamic Bearer Type Switching 56

2.3.1 Measurement-based Analysis of Dynamic Bearer Type Switching 57

References 60

3 Modelling of Link Layer Characteristics 61
Wolfgang Karner

3.1 Modelling Erroneous Channels – A Literature Survey 61

3.2 Link Error Models for the UMTSDCH 66

3.2.1 Link Error Modelling – ‘Dynamic’ Case 67

3.2.2 Link Error Modelling – ‘Static’ Case 69

3.3 Impact of Channel Modelling on the Quality of Services for Streamed Video 75

3.3.1 Compared Models 76

3.3.2 Experimental Setup 76

3.3.3 Simulation Results for H.264 Encoded Video over Error Prone Links 78

3.4 A Dynamic Bearer Type Switching Model 83

3.4.1 Four-state Markov Model 83

3.4.2 Enhanced Four-state Model 84

References 86

4 Analysis of Link Error Predictability in the UTRAN 89
Wolfgang Karner

4.1 Prediction of Low Error Probability Intervals 90

4.1.1 Detection of Start of  Intervals 90

4.1.2 Interval Length Li 91

4.2 Estimation of Expected Failure Rate 92

References 95

III Video Coding and Error Handling 97

5 Principles of Video Coding 101
Olivia Nemethova

5.1 Video Compression 101

5.1.1 Video Sampling 101

5.1.2 Compression Mechanisms 103

5.1.3 Structure of Video Streams 107

5.1.4 Profiles and Levels 108

5.1.5 Reference Software 108

5.2 H.264/AVC Video Streaming in Error-prone Environment 109

5.2.1 Error Propagation 109

5.2.2 Standardized Error Resilience Techniques 110

5.2.3 Alternative Error Resilience Techniques 111

5.3 Error Concealment 112

5.3.1 Spatial Error Concealment 113

5.3.2 Temporal Error Concealment Methods 115

5.4 Performance Indicators 118

References 120

6 Error Detection Mechanisms for Encoded Video Streams 125
Luca Superiori, Claudio Weidmann and Olivia Nemethova

6.1 Syntax Analysis 126

6.1.1 Structure of VCL NALUs 126

6.1.2 Rules of Syntax Analysis 128

6.1.3 Error-handling Mechanism 131

6.1.4 Simulation Setup 133

6.1.5 Subjective Quality Comparison 134

6.1.6 Detection Performance 135

6.2 Pixel-domain Impairment Detection 137

6.2.1 Impairments in the Inter Frames 137

6.2.2 Impairments in the Intra Frames 138

6.2.3 Performance Results 139

6.3 Fragile Watermarking 140

6.4 VLC Resynchronization 146

6.4.1 Signalling of Synchronization Points 146

6.4.2 Codes for Length Indicators 148

6.5 From Error Detection to Soft Decoding 151

6.5.1 Sequential CAVLC Decoder 152

6.5.2 Additional Synchronization Points 153

6.5.3 Postprocessing 154

6.5.4 Performance 154

References 157

IV Error Resilient Video Transmission over UMTS 159

7 3GPP Video Services – Video Codecs, Content Delivery Protocols and Optimization Potentials 163
Thomas Stockhammer and Jiangtao Wen

7.1 3GPP Video Services 163

7.1.1 Introduction 163

7.1.2 System Overview 164

7.1.3 Video Codecs in 3GPP 166

7.1.4 Bearer and Transport QoS 169

7.1.5 QoS using Video Error Resilience 171

7.2 Selected QoS Tools–Principles and Experimental Results 171

7.2.1 3GDedicatedChannelLinkLayer 171

7.2.2 Experimental Results for Conversational Video 173

7.2.3 Experimental Results for Moderate-delay Applications 175

7.2.4 System Design Guidelines 177

7.3 Selected Service Examples 178

7.3.1 Multimedia Telephony Services 178

7.3.2 Multimedia Download Delivery 180

7.3.3 Multimedia Streaming Services over MBMS 181

7.4 Conclusions 184

References 184

8 Cross-layer Error Resilience Mechanisms 187
Olivia Nemethova, Wolfgang Karner and Claudio Weidmann

8.1 Link Layer Aware Error Detection 188

8.1.1 Error Detection at RLC Layer 188

8.1.2 RLCPDU Based VLC Resynchronization 189

8.1.3 Error Detection and VLC Resynchronization Efficiency 191

8.2 Link Error Prediction Based Redundancy Control 192

8.2.1 Redundancy Control 192

8.3 Semantics-aware Scheduling 196

8.3.1 Scheduling Mechanism 196

8.3.2 Performance Evaluation 199

8.4 Distortion-aware Scheduling 202

8.4.1 Scheduling Mechanism.202

8.4.2 Distortion Estimation 203

8.4.3 Performance Evaluation 207

References 209

V Monitoring and QoS Measurement 211

9 Traffic and Performance Monitoring in a Real UMTS Network 215
Fabio Ricciato

9.1 Introduction to Traffic Monitoring 215

9.2 Network Monitoring via Traffic Monitoring: the Present and the Vision 216

9.3 AMonitoringFrameworkfor3GNetworks 219

9.4 Examples of Network-centric Applications 220

9.4.1 Optimization in the Core Network Design 220

9.4.2 Parameter Optimization 221

9.4.3 What-if Analysis 222

9.4.4 Detecting Anomalies 223

9.5 Examples of User-centric Applications 224

9.5.1 Traffic Classification 225

9.5.2 QoS and QoE monitoring 226

9.6 Summary 226

References 227

10 Traffic Analysis for UMTS Network Validation and Troubleshooting 229
Fabio Ricciato and Peter Romirer-Maierhofer

10.1 Case study: Bottleneck Detection 229

10.1.1 Motivations and Problem Statement 229

10.1.2 Input Traces 233

10.1.3 Diagnosis based on Aggregate Traffic Rate Moments 234

10.1.4 Diagnosis based on TCP Performance Indicators 239

10.2 Case Study: Analysis of One-way Delays 243

10.2.1 Motivations 243

10.2.2 Measurement Methodology 244

10.2.3 Detecting Micro Congestion Caused by High-rate Scanners 245

10.2.4 Revealing Network Equipment Problems 249

10.2.5 Exploiting One-way Delays for Online Anomaly Detection 250

References 254

11 End-to-End Video Quality Measurements 257
Michal Ries

11.1 Test Methodology for Subjective Video Testing 260

11.1.1 Video Quality Evaluation 261

11.1.2 Subjective Testing 263

11.1.3 Source Materials 263

11.2 Results of Subjective Quality Tests 265

11.2.1 Subjective Quality Tests on SIF Resolution and H.264/AVC Codec 265

11.3 Video Quality Estimation 267

11.3.1 Temporal Segmentation 267

11.3.2 Video Content Classification 268

11.3.3 Content Sensitive Features 268

11.3.4 Hypothesis Testing and Content Classification 274

11.3.5 Video Quality Estimation for SIF-H.264 Resolution 275

11.3.6 Content Based Video Quality Estimation 276

11.3.7 Ensemble Based Quality Estimation 280

References 283

VI Packet Switched Traffic – Evolution and Modelling 287

12 Traffic Description 291
Philipp Svoboda

12.1 Introduction 291

12.1.1 Analysed Traces 291

12.1.2 Daily Usage Profile for UMTS and GPRS 292

12.2 Volume and User Population 293

12.2.1 Volumes and User Population in GPRS and UMTS 293

12.2.2 Fraction of Volume per Service 296

12.2.3 Service Mix Diurnal Profile 298

12.2.4 Grouping Subscribers per Service Access 300

12.2.5 Filtering in the Port Analysis 301

12.3 Analysis of the PDP-context Activity 301

12.3.1 Per-user Activity 302

12.3.2 Distribution of PDP-context Duration 302

12.3.3 The Volume of a PDP-context 307

12.3.4 Total Volume and Number of PDP-contexts per Group 308

12.4 Detecting and Filtering of Malicious Traffic 309

References 311

13 Traffic Flows 313
Philipp Svoboda

13.1 Introduction to Flow Analysis 313

13.1.1 Heavy Tailed 314

13.1.2 The Flow 315

13.1.3 Protocol Shares 317

13.2 Fitting of Distributions to Empirical Data 317

13.2.1 Pre-evaluation of the Dataset 317

13.2.2 Parameter Estimation 318

13.2.3 Goodness of Fit 321

13.3 Flows Statistics 321

13.3.1 Evolution of the TCP/UDP and Application Flow Lengths from 2005 to 2007 321

13.3.2 Example Validation of the Datasets 322

13.3.3 Scaling Analysis of the Heavy Tail Parameter 323

13.3.4 Fitting Flow Size and Duration 324

13.3.5 Mice and Elephants in Traffic Flows 328

References 330

14 Adapting Traffic Models for High-delay Networks 333
Philipp Svoboda

14.1 Motivation 333

14.2 Modelling HTTP Browsing Sessions for the Mobile Internet Access 335

14.2.1 HTTP Traffic Model 337

14.3 Modelling FTP Sessions in a Mobile Network 341

14.3.1 Modelling FTP Sessions 342

14.3.2 Fitting the Parameters 343

14.4 Email Traffic Model: An Extension to High-delay Networks 344

14.4.1 Email Protocols of the Internet 344

14.4.2 APOP3EmailModel for High RTT Networks 346

14.4.3 Simulation Setup 350

14.4.4 Simulation Results 352

References 352

15 Traffic Models for Specific Services 355
Philipp Svoboda

15.1 Traffic Models for Online Gaming 356

15.1.1 Traffic Model for a Fast Action Game: Unreal Tournament 358

15.1.2 Traffic Model for a Real Time Strategy Game: StarCraft 361

15.1.3 Traffic Model for a Massive Multiplayer Online Game: World of Warcraft 362

15.2 A Traffic Model for Push-to-Talk (Nokia) 370

15.2.1 AMR: Facts from the Data Sheets 371

15.2.2 Parameters for Artificial Conversational Speech 372

15.2.3 PTT Model 372

References 374

Index 377