Coffee: Cost-effective edge caching for live 360 degree video streaming

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 framework (Coffee) for live 360 degree video that employs collaborative FoV prediction and predictive tile prefetching to reduce bandwidth consumption and streaming cost, and improve streaming quality and robustness. By utilizing the viewers’ playback latency gaps and exploiting the unique tile consumption patterns of live 360 degree video streaming, our efficient caching algorithms achieve substantial 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 backhaul traffic by 76% compared to state-of-the-art live 360 edge caching algorithms. In addition, we design a transcoding-aware edge caching variant, called TransCoffee. We assess TransCoffee through extensive experiments, which reveal that it can reduce costs by 63% compared to cutting-edge transcoding-aware methods.