NOVA: QoE-driven Optimization of DASH-based Video Delivery in Networks
This work addresses video streaming optimization for network providers and users, but it is incremental as it builds on existing DASH frameworks and QoE models.
The paper tackles the problem of optimizing video delivery in networks by jointly allocating network resources and adapting video quality to maximize Quality of Experience (QoE) fairly, balancing mean quality, variability, and fairness. It presents NOVA, an asymptotically optimal online algorithm that distributes tasks between network controllers and clients with minimal communication.
We consider the problem of optimizing video delivery for a network supporting video clients streaming stored video. Specifically, we consider the problem of jointly optimizing network resource allocation and video quality adaptation. Our objective is to fairly maximize video clients' Quality of Experience (QoE) realizing tradeoffs among the mean quality, temporal variability in quality, and fairness, incorporating user preferences on rebuffering and cost of video delivery. We present a simple asymptotically optimal online algorithm, NOVA, to solve the problem. NOVA is asynchronous, and using minimal communication, distributes the tasks of resource allocation to network controller, and quality adaptation to respective video clients. Video quality adaptation in NOVA is also optimal for standalone video clients, and is well suited for use with DASH framework. Further, we extend NOVA for use with more general QoE models, networks shared with other traffic loads and networks using fixed/legacy resource allocation.