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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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Proceedings ArticleDOI

iOverlay: a lightweight middleware infrastructure for overlay application implementations

TL;DR: iOverlay is presented, a lightweight and high-performance middleware infrastructure that addresses the problems of fully distributed algorithms or protocols in application-layer overlay networks in a novel way by providing clean, well-documented layers of middleware components.
Journal ArticleDOI

Network Coding Protocols for Smart Grid Communications

TL;DR: A robust network coding protocol that adopts the transmission of sparsely coded packets at the beginning of the transmission process but then switches to a denser coding structure towards the end, which achieves 100% reliability, even for hostile network conditions.
Book ChapterDOI

Efficient error recovery using network coding in underwater sensor networks

TL;DR: An efficient error-recovery scheme that carefully couples network coding and multipath routing is proposed and it is demonstrated that the scheme is efficient in both error recovery and energy consumption.
Proceedings ArticleDOI

An overview of network coding for multimedia streaming

TL;DR: The objective of this paper is to survey recent developments of network coding, with specific focus on multimedia streaming, and review some recent works that develop network coding principles in media streaming applications.
Proceedings ArticleDOI

On MAC Scheduling and Packet Combination Strategies for Practical Random Network Coding

TL;DR: Practical algorithms to efficiently exploit random network coding for data delivery in multi-hop wireless networks, couple several MAC and scheduling schemes together with different network coding strategies, and compare them via extensive ns2 simulation are investigated.
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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