Distributed Distortion Optimization for Correlated Sources with Network Coding

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; e...

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Veröffentlicht in:IEEE transactions on communications Jg. 60; H. 5; S. 1336 - 1344
Hauptverfasser: Tao Cui, Lijun Chen, Ho, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.05.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Applied sciences
Coding
Coding, codes
Convergence
Correlation
Data compression
Distortion
Exact sciences and technology
Information, signal and communications theory
multicast
Network coding
Networks
Optimization
rate allocation
Rate-distortion
Resource management
Signal and communications theory
Source coding
Switching and signalling
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
Wyner-Ziv
Lossy compression
Wyner-Ziv
Packet switching
High resolution
Source coding
Data compression
Resource allocation
Rate distortion theory
rate allocation
Time variation
Data broadcast
Information dissemination
Optimization
Convergence speed
Multicast
Distributed algorithm
Convergence rate
Network coding
Numerical simulation
Wyner Ziv problem
Resource management
ISSN:0090-6778, 1558-0857
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Beschreibung
Zusammenfassung: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.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2012.032012.100791