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A real-time multimedia streaming transmission control mechanism based on edge cloud computing and opportunistic approximation optimization

机译:基于边缘云计算和机会近似优化的实时多媒体流传输控制机制

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摘要

We address the problem of real-time transmission of multimedia streams in distributed heterogeneous networks. The effect of this problem directly affects the execution efficiency, real-time and reliability of the network system. Firstly, for distributed multimedia data, the heterogeneity of edge cloud devices is considered. Through in-depth analysis of the dependency between multimedia packets and video frames, the edge cloud and its dependent directed acyclic graph are established, and the edge cloud computing model is set up to execute cost, real-time and dependability. Secondly, based on the deadline features of multimedia real-time applications, the optimal solution of the edge cloud sets is established. The establishment basis is from the maximum satisfiability problem and the search for the best edge cloud with the execution cost and execution time of the multimedia streaming real-time communication application. According to the above models, a real-time multimedia streaming transmission control mechanism upon edge cloud computing and the opportunistic approximation optimization is proposed. Through the simulation and analysis experiments of static network topology and dynamic network topology, the performance of the execution cost, delay and packet loss rate of the proposed mechanism is deeply analyzed and verified. The analysis results show that the proposed mechanism can transparently influence the dynamic network topology and find an optimal solution for the guarantee of real-time and reliability through the deep fusion edge cloud computing and approximate optimization.
机译:我们解决了分布式异构网络中多媒体流的实时传输问题。该问题的影响直接影响网络系统的执行效率,实时性和可靠性。首先,对于分布式多媒体数据,考虑了边缘云设备的异构性。通过深入分析多媒体数据包与视频帧之间的相关性,建立了边缘云及其相关的有向无环图,并建立了边缘云计算模型以实现成本,实时性和可靠性。其次,基于多媒体实时应用的截止期限特征,建立了边缘云集的最优解决方案。建立的基础是最大的可满足性问题,以及利用多媒体流实时通信应用程序的执行成本和执行时间来寻找最佳边缘云。根据以上模型,提出了一种基于边缘云计算的实时多媒体流传输控制机制和机会近似优化。通过静态网络拓扑和动态网络拓扑的仿真分析实验,对所提机制的执行成本,时延和丢包率等性能进行了深入的分析和验证。分析结果表明,所提出的机制可以透明地影响动态网络拓扑,并通过深度融合边缘云计算和近似优化为保证实时性和可靠性找到了最优的解决方案。

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