A Two-Tier System for On-Demand Streaming of 360 Degree Video Over Dynamic Networks
A Two-Tier System for On-Demand Streaming of 360 Degree Video Over Dynamic Networks
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DOI：
10.1109/jetcas.2019.2898877
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发表时间：
2019-02
影响因子：
4.6
通讯作者：
Liyang Sun;F. Duanmu;Y. Liu;Yao Wang;Y. Ye;Hang Shi;David H. Dai
中科院分区：
文献类型：
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作者：
Liyang Sun;F. Duanmu;Y. Liu;Yao Wang;Y. Ye;Hang Shi;David H. Dai
360°视频点播流是新兴的虚拟现实和增强现实应用的关键组成部分。在这样的应用中，发送整个360°视频需要极高的网络带宽，这可能是当今网络无法承受的。另一方面，仅发送预测的用户视场（FoV）是不可行的，因为在点播流中很难实现完美的FoV预测，最好提前数秒预取视频，以吸收网络带宽波动。本文提出了一个两层解决方案，其中基本层使用较长的预取缓冲区以较低的质量提供整个360°跨度，增强层使用较短的缓冲区以较高的质量提供预测的FoV。基础层提供了对网络带宽变化和视场预测误差的鲁棒性。增强层可以提高视频质量，前提是信息及时传递，并且视场预测准确。我们研究了两层之间的最佳速率分配和增强层的缓冲区配置，以实现视频质量和流鲁棒性之间的最佳权衡。我们还设计了周期性和自适应优化框架，以实时适应带宽变化和视场预测误差。通过由真实LTE和WiGig网络带宽轨迹和用户视场轨迹驱动的仿真，我们证明了所提出的两层系统在面对网络带宽和用户视场动态时可以实现高水平的体验质量。
360° video on-demand streaming is a key component of the emerging virtual reality and augmented reality applications. In such applications, sending the entire 360° video demands extremely high network bandwidth that may not be affordable by today’s networks. On the other hand, sending only the predicted user’s field of view (FoV) is not viable as it is hard to achieve perfect FoV prediction in on-demand streaming, where it is better to prefetch the video multiple seconds ahead, to absorb the network bandwidth fluctuation. This paper proposes a two-tier solution, where the base tier delivers the entire 360° span at a lower quality with a long prefetching buffer, and the enhancement tier delivers the predicted FoV at a higher quality using a short buffer. The base tier provides robustness to both network bandwidth variations and FoV prediction errors. The enhancement tier improves the video quality if it is delivered in time and FoV prediction is accurate. We study the optimal rate allocation between the two tiers and buffer provisioning for the enhancement tier to achieve the optimal trade-off between video quality and streaming robustness. We also design periodic and adaptive optimization frameworks to adapt to the bandwidth variations and FoV prediction errors in realtime. Through simulations driven by real LTE and WiGig network bandwidth traces and user FoV traces, we demonstrate that the proposed two-tier systems can achieve a high-level of quality-of-experience in the face of network bandwidth and user FoV dynamics.