A Random Linear Network Coding Approach to Multicast
Ho, T.
Medard, M.
Koetter, R.
Karger, D.R.
Effros, M.
Jun Shi
Leong, B.
Lab. for Inf. & Decision Syst., MIT, Cambridge, MA;
This paper appears in: Information Theory, IEEE Transactions on
Publication Date: Oct. 2006
Volume: 52,
Issue: 10
On page(s): 4413-4430
ISSN: 0018-9448
INSPEC Accession Number: 9116316
Digital Object Identifier: 10.1109/TIT.2006.881746
Current Version Published: 2006-09-25
Abstract
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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