Adaptive video on demand

In this paper we formulate the problem of Video on Demand (V0D) from a resource allocation perspective. In particular, we introduce the decision element into a movie vending environment, which complements the current approaches. In contrast with more the traditional resource allocation problems (such as machine scheduling and call control), the problem possesses the distinctive batchingproperty, which stands for the feasibility of several requests being served by one resource (channel). We investigate the problem in an on-line fashion, namely, having to accept or reject a request for a movie without the knowledge of future requests. We show upper and lower bounds on the competitive ratio of deterministic on-line movie scheduling algorithms for a variety of scenarios (an algorithm is called competitive if it performs, up to a constant factor, as well as its off-line, clairvoyant counterparts for the same problem). In particular, for the natural case of refusal by choice with delayed notification, we present a class of algorithms that exhibit, under certain conditions, an asymptotically optimal behavior. We also compare the performances of the different algorithms under various distributions of requests over time, and evaluate the effect of various heuristics.