QoE-Fair Video Streaming over DASH

Abstract

Video streaming has become, and is expected to remain, the dominant type of traffic over the Internet. With this high demand for multimedia streaming, there is always a question on how to provide acceptable and fair Quality of Experience (QoE) for consumers of the over-the-top video services, despite the best-effort nature of the Internet and the limited network resources, shared by concurrent users. MPEG-DASH, as one of the most widely used standards of HTTP-based adaptive streaming, uses a client-side rate adaptation algorithms; which is known to suffer from two practical challenges: in one hand, clients use fixed heuristics that have been fine-tuned according to strict assumptions about deployment environments which limit its ability to generalize across network conditions. On the other hand, the absence of collaboration among DASH clients leads to unfair bandwidth allocation, and typically ends up in an unbalanced equilibrium point. We believe that augmenting a server-side rate adaptation significantly improves the fairness of network bandwidth allocation among concurrent users. We have formulated the problem as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) model, and used RL to train two neural networks to find an optimal solution to the proposed Dec-POMDP problem in a distributed way. We showed that our proposed client-server collaboration outperforms the state-of-the-art schemes in terms of QoE-efficiency, QoE-fairness, and social welfare by as much as 16%, 21%, and 24% respectively.