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A Packet Loss Protection Scheme Joint Deterministic Network Coding and Random Linear Network Coding for H.264/AVC

机译:H.264 / AVC的分组丢失保护方案联合确定性网络编码和随机线性网络编码

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Video streaming applications over lossy packet networks is a challenging task due to a number of factors such as delay, fix bandwidth allocation, and packet loss. The conventional Forward Error Correcting (FEC) code and automatic repeat requestret (ARQ) methods will become ineffective to transmit video streaming for the reason that video frame errors appear in bursts which will lead to reconstruct the video frame very difficult over the lossy packet network. In this paper, we focus on an effective packet loss protection scheme joint deterministic network coding (DNC) and random linear network coding (RLNC) for H.264/AVC video transmission. Considering the complexity of RLNC algorithm, the transmission scheme should make some improvements. Firstly, the source node encodes data packets based on the deterministic network coding, which will improve the efficiency of coding. Secondly, RLNC process at the intermediate nodes needs to do some limited operations for the random encoding coefficient vectors to reduce the redundancy packets in the network. Thirdly, the decoding process at the destination nodes should be simplified. We demonstrate the effectiveness of this approach by system implementation and performance evaluation. Experimental results are given to demonstrate the significant improvement of H.264/AVC streaming reconstructive video quality over the lossy packet network, and the improvement is comparably more remarkable when the packets loss dramatically.
机译:由于许多因素(如延迟,修复带宽分配和数据包丢失),视频流媒体应用程序是一个具有挑战性的任务。传统的前向误差校正(FEC)代码和自动重复请求ret(ARQ)方法将无效地传输视频流,因为视频帧错误出现在突发中,这将导致在损耗数据包网络上非常困难地重建视频帧。在本文中,我们专注于有效的零件丢失保护方案接头确定性网络编码(DNC)和用于H.264 / AVC视频传输的随机线性网络编码(RLNC)。考虑到RLNC算法的复杂性,传输方案应该进行一些改进。首先,源节点基于确定性网络编码对数据分组进行编码,这将提高编码的效率。其次,中间节点处的RLNC进程需要对随机编码系数向量进行一些有限的操作,以减少网络中的冗余分组。第三,应该简化目的地节点处的解码过程。我们通过系统实施和绩效评估展示了这种方法的有效性。给出了实验结果,以证明H.264 / AVC流式重建视频质量的显着改善,在有损数据包网络上,当数据包急剧丢失时,改善更加显着。

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