QoE-fair Resource Allocation for DASH Video Delivery Systems

Services delivering videos to massive audiences are required to provide the users with a satisfactory Quality of Experience (QoE) to keep high engagement and avoid service abandonment. Adaptive BitRate algorithms (ABR) running in video players are designed to dynamically change the video bitrate to provide the best possible QoE given the user device features and the end-to-end network available bandwidth. Well-designed ABR algorithms strive to improve the individual QoE obtained by each user resulting, in the optimal case, in the maximization of the sum of QoE individually perceived by users. However, when resources are scarce, maximizing the sum of the QoE might result in favoring some clients at the expense of others which instead obtain poor QoEs with the possible consequence of service abandonment. Thus, we argue that video service providers should directly address fairness issues when designing their delivery networks so to gracefully degrade the QoE for all users when resources are scarce. This paper addresses this open issue and shows that the Multi-Commodity Flow Problem (MCFP) optimization framework is a proper methodology to achieve a QoE-fair distribution of the resources. The proposed solution is based on the bandwidth reservation approach that slices network resources and assigns similar video requests to the same network slice according to a proposed similarity metric dependent on video quality. Obtained results show that the proposed approach is able to achieve its goal and provide a fair level of QoE to heterogeneous clients.