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Published May 2012 | public
Journal Article

Distributed Distortion Optimization for Correlated Sources with Network Coding

Abstract

We consider lossy data compression in capacity-constrained networks with correlated sources. We derive, using dual decomposition, a distributed algorithm that maximizes an aggregate utility measure defined in terms of the distortion levels of the sources. No coordination among sources is required; each source adjusts its distortion level according to distortion prices fed back by the sinks. The algorithm is developed for the case of squared error distortion and high resolution coding where the rate-distortion region is known, and can be easily extended to consider achievable regions that can be expressed in a related form. Our distributed optimization framework applies to unicast and multicast with and without network coding. Numerical examples show relatively fast convergence, allowing the algorithm to be used in time-varying networks.

Additional Information

© 2012 IEEE. Paper approved by Z. Xiong, the Editor for Distributed Coding and Processing of the IEEE Communications Society. Manuscript received December 29, 2010; revised November 9, 2011. This work has been supported in part by DARPA grant N66001-06-C-2020, Caltech's Lee Center for Advanced Networking, the Okawa Foundation Research Grant, and a gift from Microsoft Research. This paper has been presented in part at the International Symposium on Information Theory (ISIT), Nice, France, June 2007.

Additional details

Created:
August 19, 2023
Modified:
October 17, 2023