Andres Kwasinski, Rochester Institute of Technology, USA Dr. Kwasinski received his Ph.D. degree in Electrical and Computer Engineering from the University of Maryland in 2004. He is currently a Professor with the Department of Computer Engineering, Rochester Institute of Technology, Rochester, New York. Prior to this he was with Texas Instruments Inc., the Department of Electrical and Computer Engineering at the University of Maryland, and Lucent Technologies. Dr. Kwasinski has been a member of the IEEE Signal Processing Magazine Editorial Board, as Associate Editor and Area Editor for over twelve years. He was Editor for the IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters, the Globecom 2010 Workshop Co-Chair and the Chair of the IEEE Multimedia Technical Committee Interest Group on Distributed and Sensor Networks for Mobile Media Computing and Applications. He is a Senior Member of the IEEE. Vinay Chande, Qualcomm Inc., USA Vinay Chande has a Ph.D. in Electrical Engineering from the University of Maryland and his engineering education from Indian Institute of Technology, Mumbai. Dr. Chande works as a Systems Engineer at Wireless Research and Development at Qualcomm Technologies Inc. His current work gives him an opportunity to participate in and witness the advances in millimeter-wave radio bands, unlicensed spectrum access and machine learning for Industrial IoT.

• Preface xi1 Introduction and Background 11.1 Simplified Model for a Communication System 21.2 Entropy and Information 31.3 Introduction to Source Coding 61.3.1 Sampling and Quantization of Signals 61.3.2 Source Coding of Quantized Signals 91.3.3 Distortion and Rate-distortion Theory 131.4 Channels, Channel Coding, and Capacity 171.4.1 Channel Models 171.4.2 Wireless Channels 191.4.3 Channel Coding and Channel Capacity 231.5 Layered Model for a Communication System 261.6 Distortion, Quality of Service, and Quality of Experience 301.6.1 Objective Measurements of Distortion or Quality 311.6.2 Subjective and Perceptually Based Measurements of Distortion or Quality 321.7 Shannon’s Separation Principle and Joint Source–Channel Coding 361.8 Major Classes of Joint Source–Channel Coding Techniques 40References 422 Source Coding and Signal Compression 432.1 Types of Sources 432.2 Lossless Compression 462.2.1 Entropy Coding 472.2.2 Predictive Coding 522.3 Lossy Compression 542.3.1 Quantization 542.3.2 Differential Coding 622.3.3 Transform Coding 632.3.4 Subband and Wavelet Coding 652.4 Embedded and Layered Coding 682.5 Coding of Practical Sources 712.5.1 Image Coding - JPEG 712.5.2 Embedded Image Coding – SPIHT 752.5.3 Video Coding 782.5.4 Speech Coding 83References 863 Channel Coding 873.1 Linear Block Codes 873.1.1 Binary Linear Block Codes 903.1.2 Generator Matrix, Parity-Check Matrix, and Syndrome Testing 913.1.3 Common Linear Block Codes 923.1.4 Error and Erasure Correction with Block Codes 953.2 Convolutional Codes 973.2.1 Code Characterization: State and Trellis Diagrams 983.2.2 Maximum Likelihood (ML) Decoding 1003.2.3 The Viterbi Algorithm 1013.2.4 Error Correction Performance 1043.3 Modified Linear Codes (Puncturing, Shortening, Expurgating, Extending, Augmenting, and Lengthening) 1053.4 Rate-Compatible Channel Codes 105References 1104 Concatenated Joint Source–Channel Coding 1114.1 Concatenated JSCC Bit Rate Allocation 1114.2 Performance Characterization 1194.2.1 Practical Source and Channel Codecs 1194.3 Application Cases 131References 1335 Unequal Error Protection Source–Channel Coding 1355.1 Effect of Channel Errors on Source Encoded Data 1355.2 Priority Encoding Transmission Schemes for Unequal Loss Protection 1425.3 Dynamic Programming Algorithm for Optimal UEP 1475.4 Unequal Error Protection Using Digital Fountain Codes 163References 1716 Source–Channel Coding with Feedback 1736.1 Joint Source–Channel Coding Formulation for a System with ACK/NACK Feedback 1736.1.1 Performance Measurement 1756.1.2 Classification of the Transmitters 1766.1.3 Decoder Structure and Design 1776.2 Packet Combining for Joint Source–Channel ARQ over Memoryless Channels 1796.2.1 Decoder Design Problem 1796.3 Pruned Tree-Structured Quantization in Noise and Feedback 1936.3.1 Pruned Tree-Structured Vector Quantizers 1946.3.2 Progressive Transmission with ACK/NACK Feedback of TSVQ-Encoded Sources 1956.3.3 Progressive Transmission and Receiver-Driven Rate Control 2046.4 Delay-Constrained JSCC Using Incremental Redundancy with Feedback 2056.4.1 System Description 2056.4.2 Optimal Source and Channel Rate Allocations Design 2086.4.3 Performance 213References 2207 Quantizers Designed for Noisy Channels 2237.1 Channel-Optimized Quantizers 2237.2 Scalar Quantizer Design 2277.3 Vector Quantizer Design 2347.4 Channel Mismatch Considerations 2457.5 Structured Vector Quantizers 249References 2558 Error-Resilient Source Coding 2578.1 Multiple-Description Coding 2578.2 Error-Resilient Coded Bit Streams 2738.2.1 Robust Entropy Coding 2738.2.2 Predictive Coding Mode Selection 279References 2819 Analog and Hybrid Digital–Analog JSCC Techniques 2839.1 Analog Joint Source–Channel Coding Techniques 2839.1.1 Analog Joint Source–Channel Coding in Vector Spaces 2839.1.2 Analog Joint Source–Channel Coding Through Artificial Neural Networks 2939.2 Hybrid Digital–Analog JSCC Techniques 297References 30210 Joint Source–Channel Decoding 30510.1 Source-Controlled Channel Decoding 30510.2 Exploiting Residual Redundancy at the Decoder 31410.2.1 The Soft Output Viterbi Algorithm (SOVA) 31510.2.2 Exploiting Residual Redundancy to Estimate A Priori Information 31810.3 Iterative Source–Channel Decoding 32310.3.1 The Channel Coding Optimal Estimation Algorithm 32810.3.2 Channel Coding Optimal Estimation Applied to JSCD 330References 33311 Recent Applications and Emerging Designs in Source–Channel Coding 33511.1 Source–Channel Coding for Wireless Sensor Networks 33511.2 Extending Network Capacity Through JSCC 34311.2.1 Video Telephony Calls as Application Example 34511.2.2 CDMA Statistical Multiplexing Resource Allocation and Flow Control 34711.2.3 Overhead from Communicating Rate-Distortion Data 35411.2.4 Analysis for Dynamic Call Traffic and Admission Control 35611.2.5 Performance Results 35811.3 Source–Channel Coding and Cognitive Radios 36411.4 Design of JSCC Schemes Based on Artificial Neural Networks 374References 378Index 381

Andres Kwasinski, Rochester Institute of Technology, USA Dr. Kwasinski received his Ph.D. degree in Electrical and Computer Engineering from the University of Maryland in 2004. He is currently a Professor with the Department of Computer Engineering, Rochester Institute of Technology, Rochester, New York. Prior to this he was with Texas Instruments Inc., the Department of Electrical and Computer Engineering at the University of Maryland, and Lucent Technologies. Dr. Kwasinski has been a member of the IEEE Signal Processing Magazine Editorial Board, as Associate Editor and Area Editor for over twelve years. He was Editor for the IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters, the Globecom 2010 Workshop Co-Chair and the Chair of the IEEE Multimedia Technical Committee Interest Group on Distributed and Sensor Networks for Mobile Media Computing and Applications. He is a Senior Member of the IEEE. Vinay Chande, Qualcomm Inc., USA Vinay Chande has a Ph.D. in Electrical Engineering from the University of Maryland and his engineering education from Indian Institute of Technology, Mumbai. Dr. Chande works as a Systems Engineer at Wireless Research and Development at Qualcomm Technologies Inc. His current work gives him an opportunity to participate in and witness the advances in millimeter-wave radio bands, unlicensed spectrum access and machine learning for Industrial IoT.

• Preface xi1 Introduction and Background 11.1 Simplified Model for a Communication System 21.2 Entropy and Information 31.3 Introduction to Source Coding 61.3.1 Sampling and Quantization of Signals 61.3.2 Source Coding of Quantized Signals 91.3.3 Distortion and Rate-distortion Theory 131.4 Channels, Channel Coding, and Capacity 171.4.1 Channel Models 171.4.2 Wireless Channels 191.4.3 Channel Coding and Channel Capacity 231.5 Layered Model for a Communication System 261.6 Distortion, Quality of Service, and Quality of Experience 301.6.1 Objective Measurements of Distortion or Quality 311.6.2 Subjective and Perceptually Based Measurements of Distortion or Quality 321.7 Shannon’s Separation Principle and Joint Source–Channel Coding 361.8 Major Classes of Joint Source–Channel Coding Techniques 40References 422 Source Coding and Signal Compression 432.1 Types of Sources 432.2 Lossless Compression 462.2.1 Entropy Coding 472.2.2 Predictive Coding 522.3 Lossy Compression 542.3.1 Quantization 542.3.2 Differential Coding 622.3.3 Transform Coding 632.3.4 Subband and Wavelet Coding 652.4 Embedded and Layered Coding 682.5 Coding of Practical Sources 712.5.1 Image Coding - JPEG 712.5.2 Embedded Image Coding – SPIHT 752.5.3 Video Coding 782.5.4 Speech Coding 83References 863 Channel Coding 873.1 Linear Block Codes 873.1.1 Binary Linear Block Codes 903.1.2 Generator Matrix, Parity-Check Matrix, and Syndrome Testing 913.1.3 Common Linear Block Codes 923.1.4 Error and Erasure Correction with Block Codes 953.2 Convolutional Codes 973.2.1 Code Characterization: State and Trellis Diagrams 983.2.2 Maximum Likelihood (ML) Decoding 1003.2.3 The Viterbi Algorithm 1013.2.4 Error Correction Performance 1043.3 Modified Linear Codes (Puncturing, Shortening, Expurgating, Extending, Augmenting, and Lengthening) 1053.4 Rate-Compatible Channel Codes 105References 1104 Concatenated Joint Source–Channel Coding 1114.1 Concatenated JSCC Bit Rate Allocation 1114.2 Performance Characterization 1194.2.1 Practical Source and Channel Codecs 1194.3 Application Cases 131References 1335 Unequal Error Protection Source–Channel Coding 1355.1 Effect of Channel Errors on Source Encoded Data 1355.2 Priority Encoding Transmission Schemes for Unequal Loss Protection 1425.3 Dynamic Programming Algorithm for Optimal UEP 1475.4 Unequal Error Protection Using Digital Fountain Codes 163References 1716 Source–Channel Coding with Feedback 1736.1 Joint Source–Channel Coding Formulation for a System with ACK/NACK Feedback 1736.1.1 Performance Measurement 1756.1.2 Classification of the Transmitters 1766.1.3 Decoder Structure and Design 1776.2 Packet Combining for Joint Source–Channel ARQ over Memoryless Channels 1796.2.1 Decoder Design Problem 1796.3 Pruned Tree-Structured Quantization in Noise and Feedback 1936.3.1 Pruned Tree-Structured Vector Quantizers 1946.3.2 Progressive Transmission with ACK/NACK Feedback of TSVQ-Encoded Sources 1956.3.3 Progressive Transmission and Receiver-Driven Rate Control 2046.4 Delay-Constrained JSCC Using Incremental Redundancy with Feedback 2056.4.1 System Description 2056.4.2 Optimal Source and Channel Rate Allocations Design 2086.4.3 Performance 213References 2207 Quantizers Designed for Noisy Channels 2237.1 Channel-Optimized Quantizers 2237.2 Scalar Quantizer Design 2277.3 Vector Quantizer Design 2347.4 Channel Mismatch Considerations 2457.5 Structured Vector Quantizers 249References 2558 Error-Resilient Source Coding 2578.1 Multiple-Description Coding 2578.2 Error-Resilient Coded Bit Streams 2738.2.1 Robust Entropy Coding 2738.2.2 Predictive Coding Mode Selection 279References 2819 Analog and Hybrid Digital–Analog JSCC Techniques 2839.1 Analog Joint Source–Channel Coding Techniques 2839.1.1 Analog Joint Source–Channel Coding in Vector Spaces 2839.1.2 Analog Joint Source–Channel Coding Through Artificial Neural Networks 2939.2 Hybrid Digital–Analog JSCC Techniques 297References 30210 Joint Source–Channel Decoding 30510.1 Source-Controlled Channel Decoding 30510.2 Exploiting Residual Redundancy at the Decoder 31410.2.1 The Soft Output Viterbi Algorithm (SOVA) 31510.2.2 Exploiting Residual Redundancy to Estimate A Priori Information 31810.3 Iterative Source–Channel Decoding 32310.3.1 The Channel Coding Optimal Estimation Algorithm 32810.3.2 Channel Coding Optimal Estimation Applied to JSCD 330References 33311 Recent Applications and Emerging Designs in Source–Channel Coding 33511.1 Source–Channel Coding for Wireless Sensor Networks 33511.2 Extending Network Capacity Through JSCC 34311.2.1 Video Telephony Calls as Application Example 34511.2.2 CDMA Statistical Multiplexing Resource Allocation and Flow Control 34711.2.3 Overhead from Communicating Rate-Distortion Data 35411.2.4 Analysis for Dynamic Call Traffic and Admission Control 35611.2.5 Performance Results 35811.3 Source–Channel Coding and Cognitive Radios 36411.4 Design of JSCC Schemes Based on Artificial Neural Networks 374References 378Index 381

Andres Kwasinski, Rochester Institute of Technology, USA Dr. Kwasinski received his Ph.D. degree in Electrical and Computer Engineering from the University of Maryland in 2004. He is currently a Professor with the Department of Computer Engineering, Rochester Institute of Technology, Rochester, New York. Prior to this he was with Texas Instruments Inc., the Department of Electrical and Computer Engineering at the University of Maryland, and Lucent Technologies. Dr. Kwasinski has been a member of the IEEE Signal Processing Magazine Editorial Board, as Associate Editor and Area Editor for over twelve years. He was Editor for the IEEE Transactions on Wireless Communications and IEEE Wireless Communications Letters, the Globecom 2010 Workshop Co-Chair and the Chair of the IEEE Multimedia Technical Committee Interest Group on Distributed and Sensor Networks for Mobile Media Computing and Applications. He is a Senior Member of the IEEE. Vinay Chande, Qualcomm Inc., USA Vinay Chande has a Ph.D. in Electrical Engineering from the University of Maryland and his engineering education from Indian Institute of Technology, Mumbai. Dr. Chande works as a Systems Engineer at Wireless Research and Development at Qualcomm Technologies Inc. His current work gives him an opportunity to participate in and witness the advances in millimeter-wave radio bands, unlicensed spectrum access and machine learning for Industrial IoT.

• Preface xi1 Introduction and Background 11.1 Simplified Model for a Communication System 21.2 Entropy and Information 31.3 Introduction to Source Coding 61.3.1 Sampling and Quantization of Signals 61.3.2 Source Coding of Quantized Signals 91.3.3 Distortion and Rate-distortion Theory 131.4 Channels, Channel Coding, and Capacity 171.4.1 Channel Models 171.4.2 Wireless Channels 191.4.3 Channel Coding and Channel Capacity 231.5 Layered Model for a Communication System 261.6 Distortion, Quality of Service, and Quality of Experience 301.6.1 Objective Measurements of Distortion or Quality 311.6.2 Subjective and Perceptually Based Measurements of Distortion or Quality 321.7 Shannon’s Separation Principle and Joint Source–Channel Coding 361.8 Major Classes of Joint Source–Channel Coding Techniques 40References 422 Source Coding and Signal Compression 432.1 Types of Sources 432.2 Lossless Compression 462.2.1 Entropy Coding 472.2.2 Predictive Coding 522.3 Lossy Compression 542.3.1 Quantization 542.3.2 Differential Coding 622.3.3 Transform Coding 632.3.4 Subband and Wavelet Coding 652.4 Embedded and Layered Coding 682.5 Coding of Practical Sources 712.5.1 Image Coding - JPEG 712.5.2 Embedded Image Coding – SPIHT 752.5.3 Video Coding 782.5.4 Speech Coding 83References 863 Channel Coding 873.1 Linear Block Codes 873.1.1 Binary Linear Block Codes 903.1.2 Generator Matrix, Parity-Check Matrix, and Syndrome Testing 913.1.3 Common Linear Block Codes 923.1.4 Error and Erasure Correction with Block Codes 953.2 Convolutional Codes 973.2.1 Code Characterization: State and Trellis Diagrams 983.2.2 Maximum Likelihood (ML) Decoding 1003.2.3 The Viterbi Algorithm 1013.2.4 Error Correction Performance 1043.3 Modified Linear Codes (Puncturing, Shortening, Expurgating, Extending, Augmenting, and Lengthening) 1053.4 Rate-Compatible Channel Codes 105References 1104 Concatenated Joint Source–Channel Coding 1114.1 Concatenated JSCC Bit Rate Allocation 1114.2 Performance Characterization 1194.2.1 Practical Source and Channel Codecs 1194.3 Application Cases 131References 1335 Unequal Error Protection Source–Channel Coding 1355.1 Effect of Channel Errors on Source Encoded Data 1355.2 Priority Encoding Transmission Schemes for Unequal Loss Protection 1425.3 Dynamic Programming Algorithm for Optimal UEP 1475.4 Unequal Error Protection Using Digital Fountain Codes 163References 1716 Source–Channel Coding with Feedback 1736.1 Joint Source–Channel Coding Formulation for a System with ACK/NACK Feedback 1736.1.1 Performance Measurement 1756.1.2 Classification of the Transmitters 1766.1.3 Decoder Structure and Design 1776.2 Packet Combining for Joint Source–Channel ARQ over Memoryless Channels 1796.2.1 Decoder Design Problem 1796.3 Pruned Tree-Structured Quantization in Noise and Feedback 1936.3.1 Pruned Tree-Structured Vector Quantizers 1946.3.2 Progressive Transmission with ACK/NACK Feedback of TSVQ-Encoded Sources 1956.3.3 Progressive Transmission and Receiver-Driven Rate Control 2046.4 Delay-Constrained JSCC Using Incremental Redundancy with Feedback 2056.4.1 System Description 2056.4.2 Optimal Source and Channel Rate Allocations Design 2086.4.3 Performance 213References 2207 Quantizers Designed for Noisy Channels 2237.1 Channel-Optimized Quantizers 2237.2 Scalar Quantizer Design 2277.3 Vector Quantizer Design 2347.4 Channel Mismatch Considerations 2457.5 Structured Vector Quantizers 249References 2558 Error-Resilient Source Coding 2578.1 Multiple-Description Coding 2578.2 Error-Resilient Coded Bit Streams 2738.2.1 Robust Entropy Coding 2738.2.2 Predictive Coding Mode Selection 279References 2819 Analog and Hybrid Digital–Analog JSCC Techniques 2839.1 Analog Joint Source–Channel Coding Techniques 2839.1.1 Analog Joint Source–Channel Coding in Vector Spaces 2839.1.2 Analog Joint Source–Channel Coding Through Artificial Neural Networks 2939.2 Hybrid Digital–Analog JSCC Techniques 297References 30210 Joint Source–Channel Decoding 30510.1 Source-Controlled Channel Decoding 30510.2 Exploiting Residual Redundancy at the Decoder 31410.2.1 The Soft Output Viterbi Algorithm (SOVA) 31510.2.2 Exploiting Residual Redundancy to Estimate A Priori Information 31810.3 Iterative Source–Channel Decoding 32310.3.1 The Channel Coding Optimal Estimation Algorithm 32810.3.2 Channel Coding Optimal Estimation Applied to JSCD 330References 33311 Recent Applications and Emerging Designs in Source–Channel Coding 33511.1 Source–Channel Coding for Wireless Sensor Networks 33511.2 Extending Network Capacity Through JSCC 34311.2.1 Video Telephony Calls as Application Example 34511.2.2 CDMA Statistical Multiplexing Resource Allocation and Flow Control 34711.2.3 Overhead from Communicating Rate-Distortion Data 35411.2.4 Analysis for Dynamic Call Traffic and Admission Control 35611.2.5 Performance Results 35811.3 Source–Channel Coding and Cognitive Radios 36411.4 Design of JSCC Schemes Based on Artificial Neural Networks 374References 378Index 381