Ebublio: Edge-Assisted Multiuser 360° Video Streaming.

As one of the most important manifestations of virtual reality (VR), 360 degrees panoramic videos in recent years have experienced booming development due to the desire for immersive and interactive experiences. Compared to traditional videos, 360 degrees videos are featured with uncertain user Field of View (FoV), more sensitive delay tolerance, and much higher bandwidth requirement, bringing unprecedented challenges to 360 degrees video streaming. Meanwhile, the development of 5G and mobile edge computing starts to pave the way for high-bandwidth low-latency video streaming. Some preliminary works focus on either individual FoV prediction or multiuser Quality of Experience (QoE) oriented cache strategy design, while how to design a holistic solution toward optimizing the overall user QoE with considerations over fairness and long-term system cost remains a nontrivial problem. In this article, we propose Ebublio, a novel intelligent edge caching framework to address the aforementioned challenges in 360 degrees video streaming. Ebublio consists of a collaborative FoV prediction (CFP) module and a long-term tile caching optimization (LTO) module to jointly optimize the longterm user QoE and system cost. The former module integrates the features of video content, user trajectory, and other users' records for combined prediction. The latter one employs the Lyapunov framework and a subgradient optimization approach toward the optimal caching replacement policy. Our trace-driven evaluation demonstrates the superiority of our framework, with about 42% improvement in FoV prediction, and 36% improvement in QoE at similar traffic consumption.