DRL360: 360-degree Video Streaming with Deep Reinforcement Learning
DRL360: 360-degree Video Streaming with Deep Reinforcement Learning
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DOI：
10.1109/infocom.2019.8737361
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Yuanxing Zhang;Pengyu Zhao;Kaigui Bian;Yunxin Liu;Lingyang Song;Xiaoming Li
中科院分区：
文献类型：
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作者：
Yuanxing Zhang;Pengyu Zhao;Kaigui Bian;Yunxin Liu;Lingyang Song;Xiaoming Li
360-近年来，由于全景相机和头戴式设备的巨大进步，学位视频越来越受欢迎。然而，由于360度视频通常具有高分辨率，因此传输内容需要极高的带宽。为了保护用户的体验质量（QoE），研究人员提出了基于瓦片的360度视频流传输系统，该系统将高/低比特率分配给视频帧的所选瓦片以用于在有限带宽上进行流传输。确定哪些区块应该以高/低速率分配是具有挑战性的，因为（1）视频回放包括太多在进行速率分配时随时间动态改变的特征;（2）大多数现有技术的系统集中于固定的一组算法以优化特定的QoE目标，而用户可能具有需要以不同方式优化的各种QoE目标。本文提出了一个基于深度强化学习（DRL）的360度视频流框架，名为DRL 360。DRL 360框架通过跨广泛的动态特征集联合优化多个QoE目标来帮助提高系统性能。基于DRL的模型基于由客户端视频播放器收集的观察自适应地为未来视频帧的瓦片分配速率。我们比较拟议的DRL 360现有的系统跟踪驱动的评估，以及进行现实世界的实验在各种各样的网络条件。评估结果表明，DRL 360可以适应所有考虑的场景，并在给定不同QoE目标的情况下，平均比最先进的方法高出20%-30%。
360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%–30% on average given different QoE objectives.