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Journal ArticleDOI

Network information flow

TLDR
This work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated, and by employing coding at the nodes, which the work refers to as network coding, bandwidth can in general be saved.
Abstract
We introduce a new class of problems called network information flow which is inspired by computer network applications. Consider a point-to-point communication network on which a number of information sources are to be multicast to certain sets of destinations. We assume that the information sources are mutually independent. The problem is to characterize the admissible coding rate region. This model subsumes all previously studied models along the same line. We study the problem with one information source, and we have obtained a simple characterization of the admissible coding rate region. Our result can be regarded as the max-flow min-cut theorem for network information flow. Contrary to one's intuition, our work reveals that it is in general not optimal to regard the information to be multicast as a "fluid" which can simply be routed or replicated. Rather, by employing coding at the nodes, which we refer to as network coding, bandwidth can in general be saved. This finding may have significant impact on future design of switching systems.

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Citations
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Journal ArticleDOI

Dynamic rate adaptation for improved throughput and delay in wireless network coded broadcast

TL;DR: This paper provides theoretical and simulation-based study of the delivery delay performance of a number of existing throughput-optimal coding schemes and uses the results to design a new dynamic rate adaptation scheme that achieves improved overall throughput-delay performance.
Journal ArticleDOI

Superposition coding cooperative relaying communications: Outage performance analysis

TL;DR: The approximated close-form expressions of the outage probabilities and the throughput of superposition coding relaying are obtained for the first time and a set of optimal solutions of system parameters are obtained by employing difference evolution arithmetic.
Journal ArticleDOI

Convolutional Codes With Maximum Column Sum Rank for Network Streaming

TL;DR: A metric known as the column sum rank that parallels the column Hamming distance when streaming over a network with link failures is introduced and a new family of convolutional codes that maximize the column Sum rank up to the code memory is introduced.
Proceedings ArticleDOI

Reliable and Secure Multishot Network Coding using Linearized Reed-Solomon Codes

TL;DR: A Welch-Berlekamp sum-rank decoding algorithm for linearized Reed-Solomon codes is provided, having quadratic complexity in the total length, and which can be adapted to handle not only errors, but also erasures, wire-tap observations and non-coherent communication.
Proceedings ArticleDOI

Efficient universal recovery in broadcast networks

TL;DR: A series of relatively simple conditions for universal recovery that apply when the number of packets is large and the distribution of packets among the nodes is well behaved are given.
References
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Book

Elements of information theory

TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI

Factor graphs and the sum-product algorithm

TL;DR: A generic message-passing algorithm, the sum-product algorithm, that operates in a factor graph, that computes-either exactly or approximately-various marginal functions derived from the global function.
Journal ArticleDOI

Noiseless coding of correlated information sources

TL;DR: The minimum number of bits per character R_X and R_Y needed to encode these sequences so that they can be faithfully reproduced under a variety of assumptions regarding the encoders and decoders is determined.
Journal ArticleDOI

Linear network coding

TL;DR: This work forms this multicast problem and proves that linear coding suffices to achieve the optimum, which is the max-flow from the source to each receiving node.
Journal ArticleDOI

Achievable rates for multiple descriptions

TL;DR: These rates are shown to be optimal for deterministic distortion measures for random variables and Shannon mutual information.
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