Abstract
To fulfill the increasing demand on low-latency content distribution, this paper considers content distribution using generation-based network coding with the belief propagation decoder. We propose a framework to design generation-based network codes via characterizing them as building an irregular graph, and design the code by evaluating the graph. The and-or tree evaluation technique is extended to analyze the decoding performance. By allowing for non-constant generation sizes, we formulate optimization problems based on the analysis to design degree distributions from which generation sizes are drawn. Extensive simulation results show that the design may achieve both low decoding cost and transmission overhead as compared to existing schemes using constant generation sizes, and satisfactory decoding speed can be achieved. The scheme would be of interest to scenarios where (1) the network topology is not known, dynamically changing, and/or has cycles due to cooperation between end users, and (2) computational/memory costs of nodes are of concern but network transmission rate is spare.
Highlights
This paper has proposed using generation-based network coding (GNC) codes with belief propagation (BP) decoding for content distribution over lossy and dynamic networks
It was showed that GNC codes can be modeled as an irregular bipartite graph and its BP decoding performance can be analyzed through an extended and-or tree analysis
Using the analysis as the design tool, we managed to design degree distributions from which generation sizes are drawn through solving an optimization problem
Summary
Low-latency content distribution to multiple users over a lossy and dynamic network is an important requirement in many emerging wireless applications. One direction of research is to treat the encoding vector (EV) of each coded packet (from a generation) as a sparse vector over the Ns original source packets (which is the same as in the straightforward sparse RLNC schemes), and use sparse variants of GE to decode This approach would succeed as soon as Ns innovative packets (across all the generations) are received. The penalty is the overhead that the decoding may not succeed as soon as Ns innovative packets are received because generations are not jointly decoded This trade of overhead for computational/memory costs may be desirable in some scenarios, in particular where such costs are constrained but network transmission rate is spare, as commonly seen in the rapidly-growing. We show that the code may achieve both low decoding costs and transmission overhead, as compared to using constant generation sizes [14,24]
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