The generation-based network coding has been successfully employed in overlay networks. The key idea is the generation-marking for packets along with the suitable control of buffering and transmission opportunity at n...
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(纸本)9780769533544
The generation-based network coding has been successfully employed in overlay networks. The key idea is the generation-marking for packets along with the suitable control of buffering and transmission opportunity at nodes in network. Building on recent work by Chou et al., who present the generation-based network coding for multicast networks, we extend this framework to arbitrary networkcoding problems over networks with delay. It is shown that generation-based technique can transform the nonzero-delay network into the zero-delay network and however, inappropriate transmission opportunity or short of memory at nodes may change the topology of the original network.
The throughput gain obtained by linear networkcoding (LNC) grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottle...
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The throughput gain obtained by linear networkcoding (LNC) grows as the generation size increases, while the decoding complexity also grows exponentially. High decoding complexity makes the decoder to be the bottleneck for high speed and large data transmissions. In order to reduce the decoding complexity of networkcoding, a segment linear networkcoding (SLNC) scheme is proposed. SLNC provides a general coding structure for the generation-based network coding. By dividing a generation into several segments and restraining the coding coefficients of the symbols within the same segment, SLNC splits a high-rank matrix inversion into several low-rank matrix inversions, therefore reduces the decoding complexity dramatically. In addition, two coefficient selection strategies are proposed for both centrally controlled networks and distributed networks respectively. The theoretical analysis and simulation results prove that SLNC achieves a fairly low decoding complexity at a cost of rarely few extra transmissions.
Most current-generation P2P content distribution protocols use fine-granularity blocks to distribute content to all the peers in a decentralized fashion. Such protocols often suffer from a significant degree of imbala...
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Most current-generation P2P content distribution protocols use fine-granularity blocks to distribute content to all the peers in a decentralized fashion. Such protocols often suffer from a significant degree of imbalance in block distributions, especially when the users are highly dynamic. As certain blocks become rare or even unavailable, content availability and download efficiency are adversely affected. Randomized networkcoding may improve block diversity and availability in P2P networks, as coded blocks are equally innovative and useful to peers. However, the computational complexity of networkcoding mandates that, in reality, networkcoding needs to be performed within segments, each containing a subset of blocks. In this paper, we quantitatively evaluate how networkcoding may improve content availability, block diversity, and download performance in the presence of churn, as the number of blocks in each segment for coding varies. based on stochastic models and a differential equation approach, we explore the fundamental tradeoff between the resilience gain of networkcoding to peer dynamics and its inherent coding complexity. We conclude that a small number of blocks in each segment is sufficient to realize the major benefits of networkcoding, with acceptable coding cost.
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