Predicting mobile video inter-download times with Hidden Markov Models

Saving energy in mobile networks can be achieved by intelligently deactivating basestations. One way to know when to activate or deactivate basestations is understanding user behavior. In this paper, we look at the segment download behavior of users of mobile video streaming in adaptive bit-rate streaming systems with segmented downloads. From a large trace of HTTP requests we extract mobile video sessions and their inter-download times. We evaluate if Hidden Markov Models are feasible to model and predict when a user will request segments in the future. We further analyse how choosing model parameters influences the prediction quality.