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Optimized mesh routing with intermediate recovery for error resilient delivery of MD coded image/video content

机译:具有中间恢复功能的优化网格路由,可弹性恢复MD编码的图像/视频内容

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Multiple Description (MD) source coding is a technique that breaks a media stream into equally important sub streams which can be sent over different paths for protection against wireless channel errors. In this paper, we explore the possibility of sending these descriptions through different paths that merge at some specific intermediate nodes, where the corrupted descriptions are recovered from the uncorrupted ones, thereby increasing the quality of received image/video at the destination. To quantify the gain with intermediate recovery, we first devise an analytic model with simplifying assumptions on the network system for quantifying end-to-end distortion of MD coded data as a function of path parameters, and demonstrate that on a Lena image transmitted over long multipath routes, one intermediate recovery stage offers up to 9.2% reduction in distortion compared to the traditional multipath transport. Next, accounting the random network topology we formulate mesh route construction as a cross-layer optimization problem to balance between end-to-end packet delivery delay and distortion. Since this problem is highly complex, we propose two alternative delay/distortion minimization heuristics. Further, a jointly delay and distortion optimizing genetic algorithm based meta-heuristic route construction technique is suggested for networks with highly varying link quality. NS2-based simulations of a realistic network scenario demonstrate that, in terms of peak signal-to-noise ratio the intermediate recovery approach results in substantial improvement, close to 15 dB, in quality of video delivery.
机译:多描述(MD)源编码是一种将媒体流分成同等重要的子流的技术,可以通过不同的路径发送该子流以防止无线信道错误。在本文中,我们探索了通过不同路径发送这些描述的可能性,这些路径在某些特定的中间节点处合并,在这些中间节点中,损坏的描述是从未损坏的描述中恢复的,从而提高了目的地接收图像/视频的质量。为了量化中间恢复的增益,我们首先设计了一个简化模型的分析模型,该模型简化了网络系统上量化MD编码数据的端到端失真作为路径参数的函数,并证明了在长时间传输的Lena图像上与多路径传输相比,一个多路径路径的中间恢复阶段可将失真降低多达9.2%。接下来,考虑到随机网络拓扑,我们将网状路由构造公式化为跨层优化问题,以平衡端到端数据包传递延迟和失真。由于此问题非常复杂,因此我们提出了两种替代的延迟/失真最小化启发式方法。此外,针对链路质量变化较大的网络,提出了一种基于联合时延和失真优化的遗传算法的元启发式路由构造技术。基于NS2的现实网络场景模拟表明,就峰值信噪比而言,中间恢复方法可显着提高视频传输质量,接近15 dB。

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