Distributed source and joint source-channel coding

Organizers: Javier Garcia-Frias (University of Delaware) and Zixiang Xiong (Texas A&M University)

Distributed signal processing is gaining extraordinary importance, mainly as a key contributor in many practical applications such as sensor networks, multi-terminal communication networks and wireless video. In this special session, we focus on a very specific (but fundamental) component of distributed signal processing: source and joint source-channel coding.

Practical schemes for distributed source/channel coding aim at exploiting the dependences existing between the different sources in the system to either compress or transmit such sources at information rates close to the theoretical limits. As opposed to non-distributed scenarios, the statistics of the sources are not required at the encoder site. Instead, they are exploited only at the decoder, and under certain conditions they can also be estimated jointly with the decoding process. This leads to systems in which most of the complexity is translated to the receiver site.

Distributed source and joint source-channel coding is an area of relevance in signal processing, with applications ranging from video compression to minimization of the transmitted energy in sensor networks. However, very few publications in this area have appeared under the umbrella of the Signal Processing Society. The objective of this session is to contribute to the diffusion of these techniques in the Signal Processing community.

Overview lecture:
  Authors: Javier Garcia-Frias (University of Delaware) and Zixiang Xiong (Texas A&M University)
  Title: Distributed Source and Joint Source-Channel Coding: From Theory to Practice

Regular lectures:

• Title: Towards Bridging the Gap between Theory and Practice for the Slepian-Wolf Problem
  Authors: Todd Coleman (MIT), Michelle Effros (Caltech), and Muriel Medard (MIT)
• Title: Wyner-Ziv Coding for Half-Duplex Relay Channels
  Authors: Zhixin Liu, Vladimir Stankovic, and Zixiang Xiong (Texas A&M University)
• Title: Network Correlated Data Gathering: Theory, Algorithms and Scaling Laws
  Authors: Razvan Cristescu, Baltasar Beferull-Lozano, Martin Vetterli, and Deepak Ganesan (EPFL)
• Title: Design of Optimal Quantizers for Distributed Coding of Noisy Sources
  Authors: David Rebollo-Monedero and Bernd Girod (Stanford University)
• Title: On Enhancing MPEG Video Broadcast over Wireless Networks with an Auxiliary Broadcast Channel
  Authors: Jiajun Wang, Abhik Majumdar, and Kannan Ramchandran (UC Berkeley)