A Random Linear Network Coding Approach to Multicast

We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity...

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Bibliographic Details
Published in:IEEE transactions on information theory Vol. 52; no. 10; pp. 4413 - 4430
Main Authors: Ho, T., Medard, M., Koetter, R., Karger, D.R., Effros, M., Jun Shi, Leong, B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.10.2006
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Applied sciences
Artificial intelligence
Associate members
Capacity planning
Codes
Coding
Coding, codes
Communications networks
Compressing
Computer networks
Computer science
Data compression
Data transmission
Derivation
Distributed compression
distributed networking
Exact sciences and technology
Information, signal and communications theory
Laboratories
Links
Multicast
Network coding
Networks
random linear coding
Robustness
Routing
Signal and communications theory
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Transmission and modulation (techniques and equipments)
Vectors
network coding
Data compression
Mapping
Distributed compression
Random coding
Failures
Data broadcast
Information dissemination
Nodes
random linear coding
Multicast
Linear coding
Decentralized system
Network routing
Robustness
distributed networking
ISSN:0018-9448, 1557-9654
Online Access:Get full text
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Description
Summary:We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks
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ISSN:0018-9448
1557-9654
DOI:10.1109/TIT.2006.881746