mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming
mDASH: A Markov Decision-Based Rate Adaptation Approach for Dynamic HTTP Streaming
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DOI：
10.1109/tmm.2016.2522650
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发表时间：
2016-01
影响因子：
7.3
通讯作者：
Chao Zhou;Chia-Wen Lin;Zongming Guo
中科院分区：
文献类型：
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作者：
Chao Zhou;Chia-Wen Lin;Zongming Guo
HTTP上的动态自适应流传输（DASH）最近已被广泛部署在互联网中。然而，它不施加任何自适应逻辑来选择客户端所请求的视频片段的质量。在本文中，我们提出了一种新的马尔可夫决策为基础的DASH速率自适应方案，旨在最大限度地提高用户体验的质量下随时间变化的信道条件。为此，我们提出的方法考虑到这些关键因素，使视觉质量的关键影响，包括视频播放质量，视频速率切换频率和幅度，缓冲区上溢/下溢，和缓冲区占用。此外，为了降低计算复杂度，我们提出了一种低复杂度的次优贪婪算法，适用于实时视频流。我们在网络测试床和真实世界的互联网上的实验都表明，所提出的方法在客观和主观视觉质量的良好性能。
Dynamic adaptive streaming over HTTP (DASH) has recently been widely deployed in the Internet. It, however, does not impose any adaptation logic for selecting the quality of video fragments requested by clients. In this paper, we propose a novel Markov decision-based rate adaptation scheme for DASH aiming to maximize the quality of user experience under time-varying channel conditions. To this end, our proposed method takes into account those key factors that make a critical impact on visual quality, including video playback quality, video rate switching frequency and amplitude, buffer overflow/underflow, and buffer occupancy. Besides, to reduce computational complexity, we propose a low-complexity sub-optimal greedy algorithm which is suitable for real-time video streaming. Our experiments in network test-bed and real-world Internet all demonstrate the good performance of the proposed method in both objective and subjective visual quality.