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Heavy-Traffic Analysis of QoE Optimality for On-Demand Video Streams Over Fading Channels

机译:Qoe-Demand Video Stops的QoE最优性的重型交通分析

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This paper proposes online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels, and we prove that it remains optimal under some mild conditions. We then formulate a network utility maximization problem based on the QoE of each flow. We demonstrate that our policies achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation results validate that the proposed policy indeed outperforms existing policies.
机译:本文提出了在线调度政策,以优化无线网络中的视频点播应用程序的经验质量(QoE)。我们考虑访问点(AP)向客户端向客户端发送视频内容的无线系统。每个流的QoE通过其视频播放中断的持续时间来测量。我们专门对在重型交通方案中运营的系统感兴趣。我们首先考虑一个特殊的开关渠道的案例,并建立一个调度政策,该政策可以在繁重的交通条件下实现容量区域的各个点。然后,此政策延长了更多的一般衰落渠道,我们证明它在一些温和条件下它仍然是最佳的。然后,我们基于每个流的QoE制定网络实用程序最大化问题。我们展示我们的政策在正确选择参数时实现了最佳的整体实用程序。最后,我们将我们的政策与三个流行的政策进行比较。仿真结果验证了拟议的政策确实优于现有的政策。

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