Covers video encoding and decoding, introducing H.264/AVC video codec,
Table of Contents
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
