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首页> 外文期刊>Multimedia, IEEE Transactions on >Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection
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Resource-Efficient Mobile Multimedia Streaming With Adaptive Network Selection

机译：具有自适应网络选择的资源高效的移动多媒体流

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摘要

From the advancements of mobile display and network infrastructure, mobile users can enjoy high quality mobile video streaming anywhere, anytime. However, most mobile users are still reluctant to use high quality video streaming when they are mobile due to costly cellular data and high energy consumption. In this work, we develop scheduling algorithms for resource-efficient mobile video streaming, which minimize the weighted sum objective of cellular cost and energy consumption. We first model the scheduling problem as a Markov decision process and propose an optimal scheduling algorithm based on dynamic programming. Then, we derive a heuristic algorithm that approximates the optimal algorithm. To evaluate the performance of proposed algorithms, we run simulation over YouTube video traces with audience retention graphs and mobility/connectivity traces in public transportation (e.g., commuting). Through extensive simulations, we show that our proposed scheduling algorithm has negligible performance loss compared to the optimal scheduling algorithm, where it saves 59% of cellular cost and 41% of energy compared to the YouTube default scheduler. We also implement our scheduling algorithm on an Android platform, and experimentally evaluate the performance compared to existing streaming policies.
机译：随着移动显示和网络基础设施的发展，移动用户可以随时随地享受高质量的移动视频流。然而，由于昂贵的蜂窝数据和高能耗，大多数移动用户在移动时仍不愿意使用高质量的视频流。在这项工作中，我们开发了用于资源节约型移动视频流的调度算法，该算法可最大程度地降低蜂窝式成本和能耗的加权和目标。我们首先将调度问题建模为马尔可夫决策过程，然后提出基于动态规划的最优调度算法。然后，我们得出一种启发式算法，该算法逼近最佳算法。为了评估提出的算法的性能，我们对YouTube视频轨迹进行了模拟，并在公共交通中（例如上下班）使用了观众保留图和移动性/连通性轨迹。通过广泛的仿真，我们表明，与最佳调度算法相比，我们提出的调度算法的性能损失可忽略不计，与YouTube默认调度程序相比，该算法可节省59％的蜂窝成本和41％的能源。我们还将在Android平台上实现我们的调度算法，并通过实验评估与现有流策略相比的性能。

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