Lossless Coding for Distributed Streaming Sources

Distributed source coding is traditionally viewed in a block coding context wherein all source symbols are known in advance by the encoders. However, many modern applications to which distributed source coding ideas are applied, are better modeled as having streaming data. In a streaming setting, so...

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Veröffentlicht in:IEEE transactions on information theory Jg. 60; H. 3; S. 1447 - 1474
Hauptverfasser: Draper, Stark C., Cheng Chang, Sahai, Anant
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Applied sciences
Codes
Coding, codes
Delays
Distributed source coding
Entropy
Exact sciences and technology
Information theory
Information, signal and communications theory
Joints
lossless source coding
Maximum likelihood decoding
Maximum likelihood method
Probability distribution
Signal and communications theory
Slepian-Wolf coding
Source coding
streaming data
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
universal decoding
Streaming
Lower bound
Block code
Lossless circuit
streaming data
Source coding
Coding circuit
Distributed source signal
Entropy
Slepian-Wolf coding
lossless source coding
Information transmission
Maximum likelihood decoding
Distributed source coding
Upper bound
Delay time
Maximum likelihood
Causality
universal decoding
ISSN:0018-9448, 1557-9654
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Zusammenfassung:Distributed source coding is traditionally viewed in a block coding context wherein all source symbols are known in advance by the encoders. However, many modern applications to which distributed source coding ideas are applied, are better modeled as having streaming data. In a streaming setting, source symbol pairs are revealed to separate encoders in real time and need to be reconstructed at the decoder with subject to some tolerable end-to-end delay. In this paper, a causal sequential random binning encoder is introduced and paired with maximum likelihood (ML) and universal decoders. The latter uses a novel weighted empirical suffix entropy decoding rule. We derive a lower bounds on the error exponent with delay for each decoder. We also provide upper bounds for the special case of streaming with decoder side information and discuss when upper and lower bounds match. We show that both ML and universal decoders achieve the same (positive) error exponents for all rate pairs inside the Slepian-Wolf achievable rate region. The dominant error events in streaming are different from those in block-coding and result in different exponents. Because the sequential random binning scheme is also universal over delays, the resulting code eventually reconstructs every source symbol correctly with probability one.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2013.2294368