Flocking-based live streaming of 360-degree video
Flocking-based live streaming of 360-degree video
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DOI：
10.1145/3339825.3391856
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发表时间：
2020-05
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影响因子：
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通讯作者：
Liyang Sun;Yixiang Mao;Tongyu Zong;Yong Liu;Yao Wang
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文献类型：
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作者：
Liyang Sun;Yixiang Mao;Tongyu Zong;Yong Liu;Yao Wang
实时360度视频流允许用户从任何角度关注现场活动，并且已经部署在一些商业平台上。然而，当前的系统只能以相对低的质量流传输视频，因为整个360度视频在有限的带宽下被递送给用户。在本文中，我们建议使用“群集”的想法，以提高实时360度视频流的边缘服务器上的视野（FoV）和缓存的预测性能。通过向流会话中的所有用户分配可变回放延迟，形成“流群”，并由群的前面的低延迟用户领导。我们提出了一个合作的FoV预测方案，其中实际的FoV信息的前面的羊群的用户被用来预测他们后面的用户。我们进一步提出了一种网络条件感知群集策略，以减少视频冻结，并增加对所有用户进行协作FoV预测的机会。群集还有助于缓存，因为由前端用户下载的视频瓦片可以由边缘服务器缓存以服务于群后端的用户，从而减少核心网络中的流量。我们提出了一个基于延迟FoV的缓存策略，并研究了在边缘服务器上应用转码的潜在收益。我们使用真实世界的用户FoV轨迹和WiGig网络带宽轨迹进行实验，以评估所提出的策略在基准上的收益。我们的实验结果表明，所提出的流媒体系统可以大致增加一倍的有效视频速率，这是一个用户的实际FoV内的视频速率相比，预测仅基于用户自己的过去的FoV轨迹，同时减少视频冻结。此外，边缘缓存可以将核心网络中的流量减少约80%，通过边缘服务器上的转码可以增加到90%。
Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. In this paper, we propose to use the idea of "flocking" to improve the performance of both prediction of field of view (FoV) and caching on the edge servers for live 360-degree video streaming. By assigning variable playback latencies to all the users in a streaming session, a "streaming flock" is formed and led by low latency users in the front of the flock. We propose a collaborative FoV prediction scheme where the actual FoV information of users in the front of the flock are utilized to predict of users behind them. We further propose a network condition aware flocking strategy to reduce the video freeze and increase the chance for collaborative FoV prediction on all users. Flocking also facilitates caching as video tiles downloaded by the front users can be cached by an edge server to serve the users at the back of the flock, thereby reducing the traffic in the core network. We propose a latency-FoV based caching strategy and investigate the potential gain of applying transcoding on the edge server. We conduct experiments using real-world user FoV traces and WiGig network bandwidth traces to evaluate the gains of the proposed strategies over benchmarks. Our experimental results demonstrate that the proposed streaming system can roughly double the effective video rate, which is the video rate inside a user's actual FoV, compared to the prediction only based on the user's own past FoV trajectory, while reducing video freeze. Furthermore, edge caching can reduce the traffic in the core network by about 80%, which can be increased to 90% with transcoding on edge server.