Near-Optimal One-Sided Scheduling for Coded Segmented Network Coding

As a variation of random linear network coding, segmented network coding (SNC) has attracted great interest in data dissemination over lossy networks due to its low computational cost. In order to guarantee the success of decoding, SNC can adopt a feedbackless forward error correction (FEC) approach...

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Vydáno v:IEEE transactions on computers Ročník 65; číslo 3; s. 929 - 939
Hlavní autoři: Tang, Bin, Yang, Shenghao, Ye, Baoliu, Guo, Song, Lu, Sanglu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.03.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptive scheduling
Coding
Decoding
Encoding
Forward error correction
Linear programming
Mathematical models
Network coding
Networks
Random linear network coding
Reliability
Scheduling
scheduling strategy
Segmentation
segmented network coding
Segments
Transfer matrices
forward error correction
Random linear network coding
segmented network coding
scheduling strategy
ISSN:0018-9340, 1557-9956
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Shrnutí:As a variation of random linear network coding, segmented network coding (SNC) has attracted great interest in data dissemination over lossy networks due to its low computational cost. In order to guarantee the success of decoding, SNC can adopt a feedbackless forward error correction (FEC) approach by applying a linear block code to the input packets before segmentation at the source node. In particular, if the empirical rank distribution of transfer matrices of segments is known in advance, several classes of coded SNC can achieve close-to-optimal decoding performance. However, the empirical rank distribution in the absence of feedback has been little investigated yet, making the whole performance of the FEC approach unknown. To close this gap, in this paper, we present the first comprehensive study on the transmission scheduling issue for the FEC approach, aiming at optimizing the rank distribution of transfer matrices with little control overhead. We propose an efficient adaptive scheduling framework for coded SNC in lossy unicast networks. This framework is one-sided (i.e., each network node forwards the segments adaptively only according to its own state) and scalable (i.e., its buffer cost will not keep on growing when the number of input packets goes to infinity). The performance of the framework is further optimized based on a linear programming approach. Extensive numerical results show that our framework performs near-optimally with respect to the empirical rank distribution.
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ISSN:0018-9340
1557-9956
DOI:10.1109/TC.2015.2435792