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Fairness-Guaranteed Transcoding Task Assignment for Viewer-Assisted Crowdsourced Livecast Services

机译:公平保证查看者辅助众包居住服务的代码转换任务分配

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Recent years have witnessed an outstanding increase in popularity of Crowdsourced Livecast Services (CLS), which is the latest trend in social media. In CLS, transcoding enormous video contents from massive broadcasters and providing high-quality CLS for global viewers with heterogeneous devices are computation-intensive as well as time-consuming. There are some schemes that design viewer-assisted transcoding scheme, but it is challenging to achieve an efficient and fair task assignment due to the dynamic of computing and communication resources. This paper introduces a viewer-assisted CLS framework and focuses on proposing an innovative fairness-guaranteed task assignment scheme, which is a key challenge in this context. Considering the dynamic nature of viewers’ computing and communication resources and stability, a dynamic programming problem with fairness and QoS constraints is formulated. To solve the problem, we devise a Fair Bandit (FB) algorithm based on the Combinatorial Multi-Armed Bandit (CMAB). Finally, the effectiveness of proposed scheme is demonstrated by trace-driven simulations.
机译:近年来,目睹了众群人普及(CLS)的普及普及,这是社交媒体的最新趋势。在CLS中,来自大规模广播公司的巨大视频内容并为具有异质设备提供全球观众的高质量CLS是计算密集型以及耗时。有一些方案设计观众辅助的转码方案,但由于计算和通信资源的动态而达到高效和公平的任务分配是挑战性的。本文介绍了一个观众辅助的CLS框架,侧重于提出创新的公平保证的任务分配方案,这是在这方面的关键挑战。考虑到观众的计算和通信资源和稳定性的动态性质,配制了公平和QoS限制的动态编程问题。为了解决问题,我们设计了一种基于组合多武装强盗(CMAB)的公平强盗(FB)算法。最后,通过痕量仿真证明了所提出的方案的有效性。

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