  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.
