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Mobile 4K / 2K / HD video streaming supported by real-time FEC raptorQ codes

机译:实时FEC raptorQ代码支持的移动4K / 2K /高清视频流

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With the growth of packet-switch networks in the mobile consumer electronics market, fountain codes are coming to play an increasingly important role as they allow high packet loss rates while still supporting high QoS. This is particularly critical for video streaming in multimedia delivery on moving handheld mobile consumer devices. A powerful forward error-correction mechanism is the fountain coding scheme implemented by RaptorQ codes, defined over GF(256), whose decoding algorithms are very complex from a computational perspective, but highly suitable for parallelization. With multimedia delivery for handheld consumer electronics in mind, this paper proposes several decoding schemes for embedded mobile SoCs, exploiting both the parallel potential of the GPU and CPU, sharing a common memory space. The OpenCL and OpenMP frameworks are used to exploit both accelerators for the roles of matrix multiplication and inversion that comprise the bulk of computation in RaptorQ decoding. Reported results indicate low-power requirements within the range of typical handheld consumer devices (1.5~2.4W) for data processing rates of dozens of Mbit/s that are fully within the real-time requirements of HD, 2K and 4K coded video streaming resolutions and beyond.
机译:随着移动消费电子市场中分组交换网络的发展,喷泉码将发挥越来越重要的作用,因为它们允许高丢包率,同时仍支持高QoS。这对于移动手持移动消费设备上的多媒体交付中的视频流特别重要。一种强大的前向纠错机制是在GF(256)上定义的RaptorQ码实现的喷泉编码方案,从计算角度来看,其解码算法非常复杂,但非常适合并行化。考虑到面向手持消费电子产品的多媒体交付,本文提出了几种针对嵌入式移动SoC的解码方案,该方案利用了GPU和CPU的并行潜力,共享一个公共的存储空间。 OpenCL和OpenMP框架用于利用两个加速器来实现矩阵乘法和求逆的作用,这些作用包括RaptorQ解码中的大量计算。报告的结果表明,在数十兆位/秒的数据处理速率下,典型手持式消费类设备(1.5〜2.4W)范围内的低功耗要求完全符合HD,2K和4K编码视频流分辨率的实时要求。超越。

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