LEAP

Dynamic Adaptive Streaming over HTTP (DASH) has emerged as a popular approach for video transmission, which brings a potential benefit for the Quality of Experience (QoE) because of its segment-based flexibility. However, the Internet can only provide no guaranteed delivery. The high dynamic of the available bandwidth may cause bitrate switching or video rebuffering, thus inevitably damaging the QoE. Besides, the frequently requested popular videos are transmitted for multiple times and contribute to most of the bandwidth consumption, which causes massive transmission redundancy. Therefore, we propose a Learning-based Edge with cAching and Prefetching (LEAP) to improve the online user QoE of adaptive video streaming. LEAP introduces caching into the edge to reduce the redundant video transmission and employs prefetching to fight against network jitters. Taking the state information of users into account, LEAP intelligently makes the most beneficial decisions of caching and prefetching by a QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we deploy the implemented prototype of LEAP in both the simulated scenario and the real Internet. Compared with all selected schemes, LEAP at least raises average bitrate by 34.4% and reduces video rebuffering by 42.7%, which leads to at least 15.9% improvement in the user QoE in the simulated scenario. The results in the real Internet scenario further confirm the superiority of LEAP.