While live 360 degree video streaming delivers immersive viewing experience, it poses significant bandwidth and latency challenges for content delivery networks. Edge servers are expected to play an important role in facilitating live streaming of 360 degree videos. In this paper, we propose a novel predictive edge caching algorithm (Coffee) for live 360 degree video that employ collaborative FoV prediction and predictive tile prefetching to reduce bandwidth consumption, streaming cost and improve the streaming quality and robustness. Our light-weight caching algorithms exploit the unique tile consumption patterns of live 360 degree video streaming to achieve high tile caching gains. Through extensive experiments driven by real 360 degree video streaming traces, we demonstrate that edge caching algorithms specifically designed for live 360 degree video streaming can achieve high streaming cost savings with small edge cache space consumption. Coffee, guided by viewer FoV predictions, significantly reduces back-haul traffic up to 76% compared to state-of-the-art edge caching algorithms. Furthermore, we develop a transcoding-aware variant (TransCoffee) and evaluate it using comprehensive experiments, which demonstrate that TransCoffee can achieve 63\% lower cost compared to state-of-the-art transcoding-aware approaches.

翻译：实时360度视频流在提供沉浸式观看体验的同时，给内容分发网络带来了显著的带宽和延迟挑战。边缘服务器有望在促进360度视频实时传输中发挥关键作用。本文提出一种针对实时360度视频的新型预测性边缘缓存算法Coffee，该算法通过协作式视场预测和预测性分片预取，降低带宽消耗与流媒体成本，同时提升流媒体质量与鲁棒性。这种轻量级缓存算法利用实时360度视频流独特的瓦片消费模式来实现高瓦片缓存增益。通过基于真实360度视频流轨迹的大量实验，我们证明专门为实时360度视频流设计的边缘缓存算法能够以较小的边缘缓存空间消耗实现显著的流媒体成本节约。借助观众视场预测的引导，Coffee相比当前最优的边缘缓存算法可减少高达76%的回程流量。此外，我们还开发了支持转码感知的变体算法TransCoffee，并通过综合实验评估表明，与当前最优的转码感知方法相比，TransCoffee可实现63%的成本降低。