Due to the broadcast nature of wireless networks they have been a natural platform for applying networkcoding (NC). Wireless networks can benefit significantly from NC due to their broadcast nature and the opportunit...
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Due to the broadcast nature of wireless networks they have been a natural platform for applying networkcoding (NC). Wireless networks can benefit significantly from NC due to their broadcast nature and the opportunity of enhancing bandwidth utilization. In this paper, we develop Multi-generation Mixing (MGM), which is a generalized approach for generation based network coding. With traditional generationbased NC sender packets are grouped in generations where encoding and decoding are performed on packets that belong to the same generation. In scenarios where losses cause insufficient reception of encoded packets, NC losses occur. NC losses are expensive;the minimum unit of loss is the loss of one generation. The proposed MGM framework allows the encoding among generations for the purpose of enhancing NC decodability. With MGM in scenarios where insufficient number of encodings received of a generation, it is still possible to recover the generation using data encoded in other generations. We develop MGM encoding and decoding approaches, and demonstrate the improvements in performance achieved by MGM. Further, a canonical analytical model for MGM networkcoding is developed, and, extensive simulations over random wireless networks experiencing random packet losses are presented.
Random linear networkcoding (RLNC) is attractive for data transfer as well as data storage and retrieval in complex and unreliable settings. The existing systematic RLNC approach first sends all source symbols in a g...
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Random linear networkcoding (RLNC) is attractive for data transfer as well as data storage and retrieval in complex and unreliable settings. The existing systematic RLNC approach first sends all source symbols in a generation without encoding followed by the coded redundant packets at the tail of the generation. This systematic tail RLNC achieves low delay when packet drops are rare;however, recovery of any dropped source symbol requires to wait for the coded packets at the end of the generation. We propose and evaluate a novel PACE RLNC approach that paces the transmissions of coded redundant packets throughout the generation of source symbols. The paced coded packets enable the recovery of dropped source symbols without waiting for the tail end of the generation. More specifically, we propose PACE-Uniform, which uniformly intersperses individual coded packets throughout the generation, and PACE-Burst, which intersperses bursts of code packets. Our extensive simulation evaluations indicate that PACE-Uniform significantly reduces the mean source symbol delay compared to tail RLNC, while achieving nearly the same loss probability. We also demonstrate that PACE-Burst generalizes the concept of pacing the redundant packet transmissions and can be flexibly tuned between PACE-Uniform and the conventional tail RLNC by controlling the number of coded packets in a burst.
In this paper, we present a simulation study of networkcoding schemes for multi-layer video streaming on multi-hop wireless networks. We consider in this study mainly two points: the packet loss due to the wireless e...
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ISBN:
(纸本)9781509041831
In this paper, we present a simulation study of networkcoding schemes for multi-layer video streaming on multi-hop wireless networks. We consider in this study mainly two points: the packet loss due to the wireless environment and the unequal importance of scalable video packets. First, we study generation based network coding schemes with static and adaptive redundancy to evaluate the effect of redundancy adaptation on packet loss rate. Second, we evaluate an extension of the Expanding Window concept with a hop-by-hop coding to highlight the effect of layer discrimination for scalable video streaming. Extensive simulations have been carried out using ns-3 for two different video test sequences with quality scalability. The simulation results show that adaptive redundancy can decrease the packet loss rate and introduce less overhead while Expanding Window can improve the PSNR of the video by reducing the base layer's packet loss.
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