Optimal scheduling of QoE-aware HTTP adaptive streaming

Recently HTTP adaptive streaming (HAS) has been leading the trend in the delivery of video content over Internet. The scope of this technology describes that a video is segmented into small intervals and encoded in different qualities for adapting the variance of network bandwidth. This method allows the client adjusting the quality of the requested stream dynamically. To the best of our knowledge, most heuristic algorithms proposed for HAS run a risk of freezing in video playback and thus induce a poor Quality of Experience (QoE). Therefore, how to maintain a good user experience becomes a more challenging task for the service provider. In this paper, we propose a optimal scheduling of QoE-aware HAS method which achieves the following QoE requirements. (1) Avoiding video streaming freezing (i.e. the client buffer underflow). (2) Minimizing the initial delay before the video streaming starts playing. Testing by the video Aladdin, experimental results show that our method can find the best QoE service for HAS under various QoE requirements and resource constraints.