Network-aware Source Coding and Communication
An introduction to the theory and techniques for achieving high quality network communication with the best possible bandwidth economy, this book focuses on network information flow with fidelity. Covering both lossless and lossy source reconstruction, it is illustrated throughout with real-world applications, including sensor networks and multimedia communications. Practical algorithms are presented, developing novel techniques for tackling design problems in joint network-source coding via collaborative multiple description coding, progressive coding, diversity routing and network coding. With systematic introductions to the basic theories of distributed source coding, network coding and multiple description coding, this is an ideal self-contained resource for researchers and students in information theory and network theory.
- Online resource
- 07 Oct 2011
- Cambridge University Press (Virtual Publishing)
- Cambridge, United Kingdom
- 67 b/w illus. 5 tables
About Nima Sarshar
Nima Sarshar is an Associate Professor of Software Systems Engineering at the University of Regina, Canada. The recipient of best paper awards at IEEE P2P 2004 and SPIE VCIP 2008, his research interests include network communication of multimedia signals, large-scale distributed computing and P2P computing. Xiaolin Wu is a Professor in the Department of Electrical and Computer Engineering at McMaster University. His research interests include multimedia signal processing and communications, data compression, and visual computing. He is an IEEE Fellow and currently serves as an Associate Editor of IEEE Transactions on Image Processing. Jia Wang is an Associate Professor in the Department of Electrical Engineering at Shanghai Jiao Tong University. His research interests include multiuser information theory and its application in video coding. Sorina Dumitrescu is an Assistant Professor in the Department of Electrical and Computer Engineering at McMaster University. Her research interests lie in robust image coding, data compression for networks, multiple description source codes, quantization and joint source-channel coding.
Table of contents
1. Introduction; Part I. The Lossless Scenario: 2. Lossless multicast with a single source; 3. Lossless multicast of multiple uncorrelated sources; 4. Lossless multicast of multiple correlated sources; Part II. The Lossy Scenario: 5. Lossy source communication: an approach based on multiple-description codes; 6. Solving the rainbow network flow problem; 7. Continuous rainbow network flow: rainbow network flow with unbounded delay; 8. Practical methods for MDC design; 9. Using progressive codes for lossy source communication; 10. Lossy communication of multiple correlated sources.