Professor Markus Rupp, University of Technology Vienna, AustriaMarkus 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 xiiiAbout the Contributors xvForeword xixPreface xxiAcknowledgements xxvList of Abbreviations xxviiI Cellular Mobile Systems 11 Introduction to Radio and Core Networks of UMTS 5Philipp Svoboda and Wolfgang Karner1.1 UMTS Network Architecture 71.2 UTRAN Architecture 81.2.1 UTRAN Protocol Architecture 91.2.2 Physical Layer Data Processing in the UTRAN Radio Interface 131.3 UMTSPS-core Network Architecture 161.4 A Data Session in a 3GNetwork 181.4.1 The UMTS (PS-core) Protocol Stack 191.4.2 The Protocols 201.4.3 Bearer Speed in UMTS 231.5 Differences between 2.5G and 3G Core Network Entities 231.5.1 GPRS Channels 241.5.2 GPRS Core Network Architecture 251.5.3 The GPRS Protocol Stack 251.5.4 Bearer Speed in GPRS and EDGE 271.6 HSDPA: an Evolutionary Step 271.6.1 Architecture of HSDPA 281.6.2 Difference between UMTS and HSDPA 291.6.3 Transport and Control Channels 31References 32II Analysis and Modelling of the Wireless Link 352 Measurement-based Analysis of UMTS Link Characteristics 39Wolfgang Karner2.1 Measurement Setup 402.1.1 General Setup 402.1.2 Mobility Scenarios 422.2 Link Error Analysis 462.2.1 Link Error Probability 462.2.2 Number of erroneous TBs in TTIs 482.2.3 TTI-burstlength,TTI-gaplength 482.2.4 TB Error Bursts, TB Error Clusters 502.2.5 The Influence of TPC on Link Error Characteristics 522.2.6 Statistical Dependency between Successive Gaps/Bursts 542.2.7 Block Error Ratio (BLER) 552.3 Dynamic Bearer Type Switching 562.3.1 Measurement-based Analysis of Dynamic Bearer Type Switching 57References 603 Modelling of Link Layer Characteristics 61Wolfgang Karner3.1 Modelling Erroneous Channels – A Literature Survey 613.2 Link Error Models for the UMTSDCH 663.2.1 Link Error Modelling – ‘Dynamic’ Case 673.2.2 Link Error Modelling – ‘Static’ Case 693.3 Impact of Channel Modelling on the Quality of Services for Streamed Video 753.3.1 Compared Models 763.3.2 Experimental Setup 763.3.3 Simulation Results for H.264 Encoded Video over Error Prone Links 783.4 A Dynamic Bearer Type Switching Model 833.4.1 Four-state Markov Model 833.4.2 Enhanced Four-state Model 84References 864 Analysis of Link Error Predictability in the UTRAN 89Wolfgang Karner4.1 Prediction of Low Error Probability Intervals 904.1.1 Detection of Start of  Intervals 904.1.2 Interval Length Li 914.2 Estimation of Expected Failure Rate 92References 95III Video Coding and Error Handling 975 Principles of Video Coding 101Olivia Nemethova5.1 Video Compression 1015.1.1 Video Sampling 1015.1.2 Compression Mechanisms 1035.1.3 Structure of Video Streams 1075.1.4 Profiles and Levels 1085.1.5 Reference Software 1085.2 H.264/AVC Video Streaming in Error-prone Environment 1095.2.1 Error Propagation 1095.2.2 Standardized Error Resilience Techniques 1105.2.3 Alternative Error Resilience Techniques 1115.3 Error Concealment 1125.3.1 Spatial Error Concealment 1135.3.2 Temporal Error Concealment Methods 1155.4 Performance Indicators 118References 1206 Error Detection Mechanisms for Encoded Video Streams 125Luca Superiori, Claudio Weidmann and Olivia Nemethova6.1 Syntax Analysis 1266.1.1 Structure of VCL NALUs 1266.1.2 Rules of Syntax Analysis 1286.1.3 Error-handling Mechanism 1316.1.4 Simulation Setup 1336.1.5 Subjective Quality Comparison 1346.1.6 Detection Performance 1356.2 Pixel-domain Impairment Detection 1376.2.1 Impairments in the Inter Frames 1376.2.2 Impairments in the Intra Frames 1386.2.3 Performance Results 1396.3 Fragile Watermarking 1406.4 VLC Resynchronization 1466.4.1 Signalling of Synchronization Points 1466.4.2 Codes for Length Indicators 1486.5 From Error Detection to Soft Decoding 1516.5.1 Sequential CAVLC Decoder 1526.5.2 Additional Synchronization Points 1536.5.3 Postprocessing 1546.5.4 Performance 154References 157IV Error Resilient Video Transmission over UMTS 1597 3GPP Video Services – Video Codecs, Content Delivery Protocols and Optimization Potentials 163Thomas Stockhammer and Jiangtao Wen7.1 3GPP Video Services 1637.1.1 Introduction 1637.1.2 System Overview 1647.1.3 Video Codecs in 3GPP 1667.1.4 Bearer and Transport QoS 1697.1.5 QoS using Video Error Resilience 1717.2 Selected QoS Tools–Principles and Experimental Results 1717.2.1 3GDedicatedChannelLinkLayer 1717.2.2 Experimental Results for Conversational Video 1737.2.3 Experimental Results for Moderate-delay Applications 1757.2.4 System Design Guidelines 1777.3 Selected Service Examples 1787.3.1 Multimedia Telephony Services 1787.3.2 Multimedia Download Delivery 1807.3.3 Multimedia Streaming Services over MBMS 1817.4 Conclusions 184References 1848 Cross-layer Error Resilience Mechanisms 187Olivia Nemethova, Wolfgang Karner and Claudio Weidmann8.1 Link Layer Aware Error Detection 1888.1.1 Error Detection at RLC Layer 1888.1.2 RLCPDU Based VLC Resynchronization 1898.1.3 Error Detection and VLC Resynchronization Efficiency 1918.2 Link Error Prediction Based Redundancy Control 1928.2.1 Redundancy Control 1928.3 Semantics-aware Scheduling 1968.3.1 Scheduling Mechanism 1968.3.2 Performance Evaluation 1998.4 Distortion-aware Scheduling 2028.4.1 Scheduling Mechanism.2028.4.2 Distortion Estimation 2038.4.3 Performance Evaluation 207References 209V Monitoring and QoS Measurement 2119 Traffic and Performance Monitoring in a Real UMTS Network 215Fabio Ricciato9.1 Introduction to Traffic Monitoring 2159.2 Network Monitoring via Traffic Monitoring: the Present and the Vision 2169.3 AMonitoringFrameworkfor3GNetworks 2199.4 Examples of Network-centric Applications 2209.4.1 Optimization in the Core Network Design 2209.4.2 Parameter Optimization 2219.4.3 What-if Analysis 2229.4.4 Detecting Anomalies 2239.5 Examples of User-centric Applications 2249.5.1 Traffic Classification 2259.5.2 QoS and QoE monitoring 2269.6 Summary 226References 22710 Traffic Analysis for UMTS Network Validation and Troubleshooting 229Fabio Ricciato and Peter Romirer-Maierhofer10.1 Case study: Bottleneck Detection 22910.1.1 Motivations and Problem Statement 22910.1.2 Input Traces 23310.1.3 Diagnosis based on Aggregate Traffic Rate Moments 23410.1.4 Diagnosis based on TCP Performance Indicators 23910.2 Case Study: Analysis of One-way Delays 24310.2.1 Motivations 24310.2.2 Measurement Methodology 24410.2.3 Detecting Micro Congestion Caused by High-rate Scanners 24510.2.4 Revealing Network Equipment Problems 24910.2.5 Exploiting One-way Delays for Online Anomaly Detection 250References 25411 End-to-End Video Quality Measurements 257Michal Ries11.1 Test Methodology for Subjective Video Testing 26011.1.1 Video Quality Evaluation 26111.1.2 Subjective Testing 26311.1.3 Source Materials 26311.2 Results of Subjective Quality Tests 26511.2.1 Subjective Quality Tests on SIF Resolution and H.264/AVC Codec 26511.3 Video Quality Estimation 26711.3.1 Temporal Segmentation 26711.3.2 Video Content Classification 26811.3.3 Content Sensitive Features 26811.3.4 Hypothesis Testing and Content Classification 27411.3.5 Video Quality Estimation for SIF-H.264 Resolution 27511.3.6 Content Based Video Quality Estimation 27611.3.7 Ensemble Based Quality Estimation 280References 283VI Packet Switched Traffic – Evolution and Modelling 28712 Traffic Description 291Philipp Svoboda12.1 Introduction 29112.1.1 Analysed Traces 29112.1.2 Daily Usage Profile for UMTS and GPRS 29212.2 Volume and User Population 29312.2.1 Volumes and User Population in GPRS and UMTS 29312.2.2 Fraction of Volume per Service 29612.2.3 Service Mix Diurnal Profile 29812.2.4 Grouping Subscribers per Service Access 30012.2.5 Filtering in the Port Analysis 30112.3 Analysis of the PDP-context Activity 30112.3.1 Per-user Activity 30212.3.2 Distribution of PDP-context Duration 30212.3.3 The Volume of a PDP-context 30712.3.4 Total Volume and Number of PDP-contexts per Group 30812.4 Detecting and Filtering of Malicious Traffic 309References 31113 Traffic Flows 313Philipp Svoboda13.1 Introduction to Flow Analysis 31313.1.1 Heavy Tailed 31413.1.2 The Flow 31513.1.3 Protocol Shares 31713.2 Fitting of Distributions to Empirical Data 31713.2.1 Pre-evaluation of the Dataset 31713.2.2 Parameter Estimation 31813.2.3 Goodness of Fit 32113.3 Flows Statistics 32113.3.1 Evolution of the TCP/UDP and Application Flow Lengths from 2005 to 2007 32113.3.2 Example Validation of the Datasets 32213.3.3 Scaling Analysis of the Heavy Tail Parameter 32313.3.4 Fitting Flow Size and Duration 32413.3.5 Mice and Elephants in Traffic Flows 328References 33014 Adapting Traffic Models for High-delay Networks 333Philipp Svoboda14.1 Motivation 33314.2 Modelling HTTP Browsing Sessions for the Mobile Internet Access 33514.2.1 HTTP Traffic Model 33714.3 Modelling FTP Sessions in a Mobile Network 34114.3.1 Modelling FTP Sessions 34214.3.2 Fitting the Parameters 34314.4 Email Traffic Model: An Extension to High-delay Networks 34414.4.1 Email Protocols of the Internet 34414.4.2 APOP3EmailModel for High RTT Networks 34614.4.3 Simulation Setup 35014.4.4 Simulation Results 352References 35215 Traffic Models for Specific Services 355Philipp Svoboda15.1 Traffic Models for Online Gaming 35615.1.1 Traffic Model for a Fast Action Game: Unreal Tournament 35815.1.2 Traffic Model for a Real Time Strategy Game: StarCraft 36115.1.3 Traffic Model for a Massive Multiplayer Online Game: World of Warcraft 36215.2 A Traffic Model for Push-to-Talk (Nokia) 37015.2.1 AMR: Facts from the Data Sheets 37115.2.2 Parameters for Artificial Conversational Speech 37215.2.3 PTT Model 372References 374Index 377

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Professor Markus Rupp, University of Technology Vienna, AustriaMarkus 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 xiiiAbout the Contributors xvForeword xixPreface xxiAcknowledgements xxvList of Abbreviations xxviiI Cellular Mobile Systems 11 Introduction to Radio and Core Networks of UMTS 5Philipp Svoboda and Wolfgang Karner1.1 UMTS Network Architecture 71.2 UTRAN Architecture 81.2.1 UTRAN Protocol Architecture 91.2.2 Physical Layer Data Processing in the UTRAN Radio Interface 131.3 UMTSPS-core Network Architecture 161.4 A Data Session in a 3GNetwork 181.4.1 The UMTS (PS-core) Protocol Stack 191.4.2 The Protocols 201.4.3 Bearer Speed in UMTS 231.5 Differences between 2.5G and 3G Core Network Entities 231.5.1 GPRS Channels 241.5.2 GPRS Core Network Architecture 251.5.3 The GPRS Protocol Stack 251.5.4 Bearer Speed in GPRS and EDGE 271.6 HSDPA: an Evolutionary Step 271.6.1 Architecture of HSDPA 281.6.2 Difference between UMTS and HSDPA 291.6.3 Transport and Control Channels 31References 32II Analysis and Modelling of the Wireless Link 352 Measurement-based Analysis of UMTS Link Characteristics 39Wolfgang Karner2.1 Measurement Setup 402.1.1 General Setup 402.1.2 Mobility Scenarios 422.2 Link Error Analysis 462.2.1 Link Error Probability 462.2.2 Number of erroneous TBs in TTIs 482.2.3 TTI-burstlength,TTI-gaplength 482.2.4 TB Error Bursts, TB Error Clusters 502.2.5 The Influence of TPC on Link Error Characteristics 522.2.6 Statistical Dependency between Successive Gaps/Bursts 542.2.7 Block Error Ratio (BLER) 552.3 Dynamic Bearer Type Switching 562.3.1 Measurement-based Analysis of Dynamic Bearer Type Switching 57References 603 Modelling of Link Layer Characteristics 61Wolfgang Karner3.1 Modelling Erroneous Channels – A Literature Survey 613.2 Link Error Models for the UMTSDCH 663.2.1 Link Error Modelling – ‘Dynamic’ Case 673.2.2 Link Error Modelling – ‘Static’ Case 693.3 Impact of Channel Modelling on the Quality of Services for Streamed Video 753.3.1 Compared Models 763.3.2 Experimental Setup 763.3.3 Simulation Results for H.264 Encoded Video over Error Prone Links 783.4 A Dynamic Bearer Type Switching Model 833.4.1 Four-state Markov Model 833.4.2 Enhanced Four-state Model 84References 864 Analysis of Link Error Predictability in the UTRAN 89Wolfgang Karner4.1 Prediction of Low Error Probability Intervals 904.1.1 Detection of Start of  Intervals 904.1.2 Interval Length Li 914.2 Estimation of Expected Failure Rate 92References 95III Video Coding and Error Handling 975 Principles of Video Coding 101Olivia Nemethova5.1 Video Compression 1015.1.1 Video Sampling 1015.1.2 Compression Mechanisms 1035.1.3 Structure of Video Streams 1075.1.4 Profiles and Levels 1085.1.5 Reference Software 1085.2 H.264/AVC Video Streaming in Error-prone Environment 1095.2.1 Error Propagation 1095.2.2 Standardized Error Resilience Techniques 1105.2.3 Alternative Error Resilience Techniques 1115.3 Error Concealment 1125.3.1 Spatial Error Concealment 1135.3.2 Temporal Error Concealment Methods 1155.4 Performance Indicators 118References 1206 Error Detection Mechanisms for Encoded Video Streams 125Luca Superiori, Claudio Weidmann and Olivia Nemethova6.1 Syntax Analysis 1266.1.1 Structure of VCL NALUs 1266.1.2 Rules of Syntax Analysis 1286.1.3 Error-handling Mechanism 1316.1.4 Simulation Setup 1336.1.5 Subjective Quality Comparison 1346.1.6 Detection Performance 1356.2 Pixel-domain Impairment Detection 1376.2.1 Impairments in the Inter Frames 1376.2.2 Impairments in the Intra Frames 1386.2.3 Performance Results 1396.3 Fragile Watermarking 1406.4 VLC Resynchronization 1466.4.1 Signalling of Synchronization Points 1466.4.2 Codes for Length Indicators 1486.5 From Error Detection to Soft Decoding 1516.5.1 Sequential CAVLC Decoder 1526.5.2 Additional Synchronization Points 1536.5.3 Postprocessing 1546.5.4 Performance 154References 157IV Error Resilient Video Transmission over UMTS 1597 3GPP Video Services – Video Codecs, Content Delivery Protocols and Optimization Potentials 163Thomas Stockhammer and Jiangtao Wen7.1 3GPP Video Services 1637.1.1 Introduction 1637.1.2 System Overview 1647.1.3 Video Codecs in 3GPP 1667.1.4 Bearer and Transport QoS 1697.1.5 QoS using Video Error Resilience 1717.2 Selected QoS Tools–Principles and Experimental Results 1717.2.1 3GDedicatedChannelLinkLayer 1717.2.2 Experimental Results for Conversational Video 1737.2.3 Experimental Results for Moderate-delay Applications 1757.2.4 System Design Guidelines 1777.3 Selected Service Examples 1787.3.1 Multimedia Telephony Services 1787.3.2 Multimedia Download Delivery 1807.3.3 Multimedia Streaming Services over MBMS 1817.4 Conclusions 184References 1848 Cross-layer Error Resilience Mechanisms 187Olivia Nemethova, Wolfgang Karner and Claudio Weidmann8.1 Link Layer Aware Error Detection 1888.1.1 Error Detection at RLC Layer 1888.1.2 RLCPDU Based VLC Resynchronization 1898.1.3 Error Detection and VLC Resynchronization Efficiency 1918.2 Link Error Prediction Based Redundancy Control 1928.2.1 Redundancy Control 1928.3 Semantics-aware Scheduling 1968.3.1 Scheduling Mechanism 1968.3.2 Performance Evaluation 1998.4 Distortion-aware Scheduling 2028.4.1 Scheduling Mechanism.2028.4.2 Distortion Estimation 2038.4.3 Performance Evaluation 207References 209V Monitoring and QoS Measurement 2119 Traffic and Performance Monitoring in a Real UMTS Network 215Fabio Ricciato9.1 Introduction to Traffic Monitoring 2159.2 Network Monitoring via Traffic Monitoring: the Present and the Vision 2169.3 AMonitoringFrameworkfor3GNetworks 2199.4 Examples of Network-centric Applications 2209.4.1 Optimization in the Core Network Design 2209.4.2 Parameter Optimization 2219.4.3 What-if Analysis 2229.4.4 Detecting Anomalies 2239.5 Examples of User-centric Applications 2249.5.1 Traffic Classification 2259.5.2 QoS and QoE monitoring 2269.6 Summary 226References 22710 Traffic Analysis for UMTS Network Validation and Troubleshooting 229Fabio Ricciato and Peter Romirer-Maierhofer10.1 Case study: Bottleneck Detection 22910.1.1 Motivations and Problem Statement 22910.1.2 Input Traces 23310.1.3 Diagnosis based on Aggregate Traffic Rate Moments 23410.1.4 Diagnosis based on TCP Performance Indicators 23910.2 Case Study: Analysis of One-way Delays 24310.2.1 Motivations 24310.2.2 Measurement Methodology 24410.2.3 Detecting Micro Congestion Caused by High-rate Scanners 24510.2.4 Revealing Network Equipment Problems 24910.2.5 Exploiting One-way Delays for Online Anomaly Detection 250References 25411 End-to-End Video Quality Measurements 257Michal Ries11.1 Test Methodology for Subjective Video Testing 26011.1.1 Video Quality Evaluation 26111.1.2 Subjective Testing 26311.1.3 Source Materials 26311.2 Results of Subjective Quality Tests 26511.2.1 Subjective Quality Tests on SIF Resolution and H.264/AVC Codec 26511.3 Video Quality Estimation 26711.3.1 Temporal Segmentation 26711.3.2 Video Content Classification 26811.3.3 Content Sensitive Features 26811.3.4 Hypothesis Testing and Content Classification 27411.3.5 Video Quality Estimation for SIF-H.264 Resolution 27511.3.6 Content Based Video Quality Estimation 27611.3.7 Ensemble Based Quality Estimation 280References 283VI Packet Switched Traffic – Evolution and Modelling 28712 Traffic Description 291Philipp Svoboda12.1 Introduction 29112.1.1 Analysed Traces 29112.1.2 Daily Usage Profile for UMTS and GPRS 29212.2 Volume and User Population 29312.2.1 Volumes and User Population in GPRS and UMTS 29312.2.2 Fraction of Volume per Service 29612.2.3 Service Mix Diurnal Profile 29812.2.4 Grouping Subscribers per Service Access 30012.2.5 Filtering in the Port Analysis 30112.3 Analysis of the PDP-context Activity 30112.3.1 Per-user Activity 30212.3.2 Distribution of PDP-context Duration 30212.3.3 The Volume of a PDP-context 30712.3.4 Total Volume and Number of PDP-contexts per Group 30812.4 Detecting and Filtering of Malicious Traffic 309References 31113 Traffic Flows 313Philipp Svoboda13.1 Introduction to Flow Analysis 31313.1.1 Heavy Tailed 31413.1.2 The Flow 31513.1.3 Protocol Shares 31713.2 Fitting of Distributions to Empirical Data 31713.2.1 Pre-evaluation of the Dataset 31713.2.2 Parameter Estimation 31813.2.3 Goodness of Fit 32113.3 Flows Statistics 32113.3.1 Evolution of the TCP/UDP and Application Flow Lengths from 2005 to 2007 32113.3.2 Example Validation of the Datasets 32213.3.3 Scaling Analysis of the Heavy Tail Parameter 32313.3.4 Fitting Flow Size and Duration 32413.3.5 Mice and Elephants in Traffic Flows 328References 33014 Adapting Traffic Models for High-delay Networks 333Philipp Svoboda14.1 Motivation 33314.2 Modelling HTTP Browsing Sessions for the Mobile Internet Access 33514.2.1 HTTP Traffic Model 33714.3 Modelling FTP Sessions in a Mobile Network 34114.3.1 Modelling FTP Sessions 34214.3.2 Fitting the Parameters 34314.4 Email Traffic Model: An Extension to High-delay Networks 34414.4.1 Email Protocols of the Internet 34414.4.2 APOP3EmailModel for High RTT Networks 34614.4.3 Simulation Setup 35014.4.4 Simulation Results 352References 35215 Traffic Models for Specific Services 355Philipp Svoboda15.1 Traffic Models for Online Gaming 35615.1.1 Traffic Model for a Fast Action Game: Unreal Tournament 35815.1.2 Traffic Model for a Real Time Strategy Game: StarCraft 36115.1.3 Traffic Model for a Massive Multiplayer Online Game: World of Warcraft 36215.2 A Traffic Model for Push-to-Talk (Nokia) 37015.2.1 AMR: Facts from the Data Sheets 37115.2.2 Parameters for Artificial Conversational Speech 37215.2.3 PTT Model 372References 374Index 377

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Professor Markus Rupp, University of Technology Vienna, AustriaMarkus 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 xiiiAbout the Contributors xvForeword xixPreface xxiAcknowledgements xxvList of Abbreviations xxviiI Cellular Mobile Systems 11 Introduction to Radio and Core Networks of UMTS 5Philipp Svoboda and Wolfgang Karner1.1 UMTS Network Architecture 71.2 UTRAN Architecture 81.2.1 UTRAN Protocol Architecture 91.2.2 Physical Layer Data Processing in the UTRAN Radio Interface 131.3 UMTSPS-core Network Architecture 161.4 A Data Session in a 3GNetwork 181.4.1 The UMTS (PS-core) Protocol Stack 191.4.2 The Protocols 201.4.3 Bearer Speed in UMTS 231.5 Differences between 2.5G and 3G Core Network Entities 231.5.1 GPRS Channels 241.5.2 GPRS Core Network Architecture 251.5.3 The GPRS Protocol Stack 251.5.4 Bearer Speed in GPRS and EDGE 271.6 HSDPA: an Evolutionary Step 271.6.1 Architecture of HSDPA 281.6.2 Difference between UMTS and HSDPA 291.6.3 Transport and Control Channels 31References 32II Analysis and Modelling of the Wireless Link 352 Measurement-based Analysis of UMTS Link Characteristics 39Wolfgang Karner2.1 Measurement Setup 402.1.1 General Setup 402.1.2 Mobility Scenarios 422.2 Link Error Analysis 462.2.1 Link Error Probability 462.2.2 Number of erroneous TBs in TTIs 482.2.3 TTI-burstlength,TTI-gaplength 482.2.4 TB Error Bursts, TB Error Clusters 502.2.5 The Influence of TPC on Link Error Characteristics 522.2.6 Statistical Dependency between Successive Gaps/Bursts 542.2.7 Block Error Ratio (BLER) 552.3 Dynamic Bearer Type Switching 562.3.1 Measurement-based Analysis of Dynamic Bearer Type Switching 57References 603 Modelling of Link Layer Characteristics 61Wolfgang Karner3.1 Modelling Erroneous Channels – A Literature Survey 613.2 Link Error Models for the UMTSDCH 663.2.1 Link Error Modelling – ‘Dynamic’ Case 673.2.2 Link Error Modelling – ‘Static’ Case 693.3 Impact of Channel Modelling on the Quality of Services for Streamed Video 753.3.1 Compared Models 763.3.2 Experimental Setup 763.3.3 Simulation Results for H.264 Encoded Video over Error Prone Links 783.4 A Dynamic Bearer Type Switching Model 833.4.1 Four-state Markov Model 833.4.2 Enhanced Four-state Model 84References 864 Analysis of Link Error Predictability in the UTRAN 89Wolfgang Karner4.1 Prediction of Low Error Probability Intervals 904.1.1 Detection of Start of  Intervals 904.1.2 Interval Length Li 914.2 Estimation of Expected Failure Rate 92References 95III Video Coding and Error Handling 975 Principles of Video Coding 101Olivia Nemethova5.1 Video Compression 1015.1.1 Video Sampling 1015.1.2 Compression Mechanisms 1035.1.3 Structure of Video Streams 1075.1.4 Profiles and Levels 1085.1.5 Reference Software 1085.2 H.264/AVC Video Streaming in Error-prone Environment 1095.2.1 Error Propagation 1095.2.2 Standardized Error Resilience Techniques 1105.2.3 Alternative Error Resilience Techniques 1115.3 Error Concealment 1125.3.1 Spatial Error Concealment 1135.3.2 Temporal Error Concealment Methods 1155.4 Performance Indicators 118References 1206 Error Detection Mechanisms for Encoded Video Streams 125Luca Superiori, Claudio Weidmann and Olivia Nemethova6.1 Syntax Analysis 1266.1.1 Structure of VCL NALUs 1266.1.2 Rules of Syntax Analysis 1286.1.3 Error-handling Mechanism 1316.1.4 Simulation Setup 1336.1.5 Subjective Quality Comparison 1346.1.6 Detection Performance 1356.2 Pixel-domain Impairment Detection 1376.2.1 Impairments in the Inter Frames 1376.2.2 Impairments in the Intra Frames 1386.2.3 Performance Results 1396.3 Fragile Watermarking 1406.4 VLC Resynchronization 1466.4.1 Signalling of Synchronization Points 1466.4.2 Codes for Length Indicators 1486.5 From Error Detection to Soft Decoding 1516.5.1 Sequential CAVLC Decoder 1526.5.2 Additional Synchronization Points 1536.5.3 Postprocessing 1546.5.4 Performance 154References 157IV Error Resilient Video Transmission over UMTS 1597 3GPP Video Services – Video Codecs, Content Delivery Protocols and Optimization Potentials 163Thomas Stockhammer and Jiangtao Wen7.1 3GPP Video Services 1637.1.1 Introduction 1637.1.2 System Overview 1647.1.3 Video Codecs in 3GPP 1667.1.4 Bearer and Transport QoS 1697.1.5 QoS using Video Error Resilience 1717.2 Selected QoS Tools–Principles and Experimental Results 1717.2.1 3GDedicatedChannelLinkLayer 1717.2.2 Experimental Results for Conversational Video 1737.2.3 Experimental Results for Moderate-delay Applications 1757.2.4 System Design Guidelines 1777.3 Selected Service Examples 1787.3.1 Multimedia Telephony Services 1787.3.2 Multimedia Download Delivery 1807.3.3 Multimedia Streaming Services over MBMS 1817.4 Conclusions 184References 1848 Cross-layer Error Resilience Mechanisms 187Olivia Nemethova, Wolfgang Karner and Claudio Weidmann8.1 Link Layer Aware Error Detection 1888.1.1 Error Detection at RLC Layer 1888.1.2 RLCPDU Based VLC Resynchronization 1898.1.3 Error Detection and VLC Resynchronization Efficiency 1918.2 Link Error Prediction Based Redundancy Control 1928.2.1 Redundancy Control 1928.3 Semantics-aware Scheduling 1968.3.1 Scheduling Mechanism 1968.3.2 Performance Evaluation 1998.4 Distortion-aware Scheduling 2028.4.1 Scheduling Mechanism.2028.4.2 Distortion Estimation 2038.4.3 Performance Evaluation 207References 209V Monitoring and QoS Measurement 2119 Traffic and Performance Monitoring in a Real UMTS Network 215Fabio Ricciato9.1 Introduction to Traffic Monitoring 2159.2 Network Monitoring via Traffic Monitoring: the Present and the Vision 2169.3 AMonitoringFrameworkfor3GNetworks 2199.4 Examples of Network-centric Applications 2209.4.1 Optimization in the Core Network Design 2209.4.2 Parameter Optimization 2219.4.3 What-if Analysis 2229.4.4 Detecting Anomalies 2239.5 Examples of User-centric Applications 2249.5.1 Traffic Classification 2259.5.2 QoS and QoE monitoring 2269.6 Summary 226References 22710 Traffic Analysis for UMTS Network Validation and Troubleshooting 229Fabio Ricciato and Peter Romirer-Maierhofer10.1 Case study: Bottleneck Detection 22910.1.1 Motivations and Problem Statement 22910.1.2 Input Traces 23310.1.3 Diagnosis based on Aggregate Traffic Rate Moments 23410.1.4 Diagnosis based on TCP Performance Indicators 23910.2 Case Study: Analysis of One-way Delays 24310.2.1 Motivations 24310.2.2 Measurement Methodology 24410.2.3 Detecting Micro Congestion Caused by High-rate Scanners 24510.2.4 Revealing Network Equipment Problems 24910.2.5 Exploiting One-way Delays for Online Anomaly Detection 250References 25411 End-to-End Video Quality Measurements 257Michal Ries11.1 Test Methodology for Subjective Video Testing 26011.1.1 Video Quality Evaluation 26111.1.2 Subjective Testing 26311.1.3 Source Materials 26311.2 Results of Subjective Quality Tests 26511.2.1 Subjective Quality Tests on SIF Resolution and H.264/AVC Codec 26511.3 Video Quality Estimation 26711.3.1 Temporal Segmentation 26711.3.2 Video Content Classification 26811.3.3 Content Sensitive Features 26811.3.4 Hypothesis Testing and Content Classification 27411.3.5 Video Quality Estimation for SIF-H.264 Resolution 27511.3.6 Content Based Video Quality Estimation 27611.3.7 Ensemble Based Quality Estimation 280References 283VI Packet Switched Traffic – Evolution and Modelling 28712 Traffic Description 291Philipp Svoboda12.1 Introduction 29112.1.1 Analysed Traces 29112.1.2 Daily Usage Profile for UMTS and GPRS 29212.2 Volume and User Population 29312.2.1 Volumes and User Population in GPRS and UMTS 29312.2.2 Fraction of Volume per Service 29612.2.3 Service Mix Diurnal Profile 29812.2.4 Grouping Subscribers per Service Access 30012.2.5 Filtering in the Port Analysis 30112.3 Analysis of the PDP-context Activity 30112.3.1 Per-user Activity 30212.3.2 Distribution of PDP-context Duration 30212.3.3 The Volume of a PDP-context 30712.3.4 Total Volume and Number of PDP-contexts per Group 30812.4 Detecting and Filtering of Malicious Traffic 309References 31113 Traffic Flows 313Philipp Svoboda13.1 Introduction to Flow Analysis 31313.1.1 Heavy Tailed 31413.1.2 The Flow 31513.1.3 Protocol Shares 31713.2 Fitting of Distributions to Empirical Data 31713.2.1 Pre-evaluation of the Dataset 31713.2.2 Parameter Estimation 31813.2.3 Goodness of Fit 32113.3 Flows Statistics 32113.3.1 Evolution of the TCP/UDP and Application Flow Lengths from 2005 to 2007 32113.3.2 Example Validation of the Datasets 32213.3.3 Scaling Analysis of the Heavy Tail Parameter 32313.3.4 Fitting Flow Size and Duration 32413.3.5 Mice and Elephants in Traffic Flows 328References 33014 Adapting Traffic Models for High-delay Networks 333Philipp Svoboda14.1 Motivation 33314.2 Modelling HTTP Browsing Sessions for the Mobile Internet Access 33514.2.1 HTTP Traffic Model 33714.3 Modelling FTP Sessions in a Mobile Network 34114.3.1 Modelling FTP Sessions 34214.3.2 Fitting the Parameters 34314.4 Email Traffic Model: An Extension to High-delay Networks 34414.4.1 Email Protocols of the Internet 34414.4.2 APOP3EmailModel for High RTT Networks 34614.4.3 Simulation Setup 35014.4.4 Simulation Results 352References 35215 Traffic Models for Specific Services 355Philipp Svoboda15.1 Traffic Models for Online Gaming 35615.1.1 Traffic Model for a Fast Action Game: Unreal Tournament 35815.1.2 Traffic Model for a Real Time Strategy Game: StarCraft 36115.1.3 Traffic Model for a Massive Multiplayer Online Game: World of Warcraft 36215.2 A Traffic Model for Push-to-Talk (Nokia) 37015.2.1 AMR: Facts from the Data Sheets 37115.2.2 Parameters for Artificial Conversational Speech 37215.2.3 PTT Model 372References 374Index 377

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