Grad: Learning for Overhead-aware Adaptive Video Streaming with Scalable Video Coding
Grad: Learning for Overhead-aware Adaptive Video Streaming with Scalable Video Coding
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DOI：
10.1145/3394171.3413512
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Yunzhuo Liu;Bo Jiang;Tian Guo;R. Sitaraman;D. Towsley;Xinbing Wang
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作者：
Yunzhuo Liu;Bo Jiang;Tian Guo;R. Sitaraman;D. Towsley;Xinbing Wang
视频流通常使用动态自适应HTTP流(DASH)来为用户提供良好的体验质量(QOE)。DASH中使用的视频主要通过诸如H.264/AVC的单层视频编码来编码。相比之下，多层视频编码(如H.264/SVC)为升级缓冲视频段的质量提供了更大的灵活性，并具有进一步改善QOE的潜力。然而，在DASH中使用SVC有两个挑战：(I)ABR算法设计的复杂性；(Ii)SVC编码开销的负面影响。在这项工作中，我们提出了一种深度强化学习方法Grad来设计ABR算法，该算法利用了SVC的质量升级机制。此外，我们量化了编码开销对DASH中SVC可实现的QOE的影响，并提出了跳跃使能混合编码(HYBJ)来缓解这种影响。通过仿真实验，我们证明了Grad-HYBJ算法比目前性能最好的ABR算法GRAD-HYBJ在QoE上的性能提高了17%。
Video streaming commonly uses Dynamic Adaptive Streaming over HTTP (DASH) to deliver good Quality of Experience (QoE) to users. Videos used in DASH are predominantly encoded by single-layered video coding such as H.264/AVC. In comparison, multi-layered video coding such as H.264/SVC provides more flexibility for upgrading the quality of buffered video segments and has the potential to further improve QoE. However, there are two challenges for using SVC in DASH: (i) the complexity in designing ABR algorithms; and (ii) the negative impact of SVC's coding overhead. In this work, we propose a deep reinforcement learning method called Grad for designing ABR algorithms that take advantage of the quality upgrade mechanism of SVC. Additionally, we quantify the impact of coding overhead on the achievable QoE of SVC in DASH, and propose jump-enabled hybrid coding (HYBJ) to mitigate the impact. Through emulation, we demonstrate that Grad-HYBJ, an ABR algorithm for HYBJ learned by Grad, outperforms the best performing state-of-the-art ABR algorithm by 17% in QoE.