Energy-Aware and Context-Aware Video Streaming on Smartphones
Energy-Aware and Context-Aware Video Streaming on Smartphones
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DOI：
10.1109/icdcs.2019.00090
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Xianda Chen;Tianxiang Tan;G. Cao
中科院分区：
文献类型：
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作者：
Xianda Chen;Tianxiang Tan;G. Cao
虽然以更高的比特率（分辨率）流式传输视频可以带来更好的体验质量（QoE），但需要在智能手机上下载和处理大量数据，从而消耗更多能源。在无线信号较弱的移动公交车上，与无线信号较强的静态环境（例如家里或咖啡馆）相比，需要花费更多的能量来维持高比特率视频流。另一方面，在振动环境（即移动的车辆）中观看高码率视频，用户感知的QoE并不会增加太多，因为视频质量的感知会受到移动公交车上的振动或晃动等环境的影响。为了解决这个问题，我们建议通过考虑视频流的上下文（环境）来节省能源。为了模拟环境的影响，我们利用智能手机中的嵌入式传感器（例如加速度计）来记录视频流期间的振动水平。基于质量评估实验，我们收集了视频比特率和振动水平对 QoE 的影响并对其进行了建模，并对视频比特率和信号强度对功耗的影响进行了建模。基于 QoE 模型和功耗模型，我们将能量感知和上下文感知视频流问题表述为优化问题。我们提出了一种可以最大化 QoE 并最小化能量的最佳算法。由于最优算法需要对未来任务有完美的了解，因此我们进一步提出了一种在线比特率选择算法。通过实际测量和跟踪驱动的模拟，我们证明在考虑能源和 QoE 时，所提出的算法可以显着优于现有方法。
Although streaming video at a higher bitrate (resolution) can lead to better Quality of Experience (QoE), a larger amount of data will have to be downloaded and processed on smartphones and thus consuming more energy. On a moving bus where the wireless signal is weak, more energy will have to be spent on maintaining high bitrate video streaming than at a static environment such as at home or a cafe where the wireless signal is strong. On the other hand, the user perceived QoE does not increase too much by watching high bitrate videos in a vibrating environment (i.e., a moving vehicle), because the perception of video quality is affected by the environment such as the vibration or shaking on a moving bus. To address this problem, we propose to save energy by considering the context (environment) of video streaming. To model the impact of context, we exploit the embedded sensors (e.g., accelerometer) in smartphones to record the vibration level during video streaming. Based on quality assessment experiments, we collect traces and model the impacts of video bitrate and vibration level on QoE, and model the impacts of video bitrate and signal strength on power consumption. Based on the QoE model and the power model, we formulate the energy-aware and context-aware video streaming problem as an optimization problem. We present an optimal algorithm which can maximize QoE and minimize energy. Since the optimal algorithm requires perfect knowledge of future tasks, we further propose an online bitrate selection algorithm. Through real measurements and trace-driven simulations, we demonstrate that the proposed algorithm can significantly outperform existing approaches when considering both energy and QoE.