Context-aware Adaptive Bitrate Streaming System

As video traffic volume increases, video streaming providers are struggling to improve the quality of experience (QoE). On the other hand, video viewers often prefer a lower traffic volume over a QoE that is too high since they contract for data-capped or pay-per-use communication plans. Thus, traffic volume should be reduced as much as possible while achieving the required QoE, which is the minimum QoE that will satisfy users. However, the required QoE depends on the context (e.g., the type of content and the preference of the user), and it is unrealistic and costly for users to set the required quality separately for each possible context. In this paper, we propose a context-aware adaptive bitrate streaming system that reduces the traffic volume while achieving the required QoE. Instead of requiring the required QoE to be configured by users, the proposed system uses the viewing time as implicit feedback on the QoE. By using this feedback, the proposed system automatically controls the QoE with a two-stage approach: it estimates the required QoE and calculates the bitrate to reduce the traffic volume while maintaining a QoE above the required QoE. To determine the required QoE based on few views, the proposed system searches for the required QoE in the mean opinion score space and uses Bayesian optimization. The results of trace-based simulations show that the proposed system can control the QoE close to the context-dependent required QoE based on fewer views than baseline algorithms.