Optimizing energy consumption and user experience in a mobile video streaming scenario

When a smartphone receives data via a mobile network, client-side energy consumption depends - among others - on the available bandwidth, which typically varies while traveling. Based on this observation, we firstly design and analyze a generic dynamic programming approach - solely at the application layer/at client's side - that can be used to optimize various target values w.r.t. mobile video streaming, and secondly develop concrete variants of our general approach in order to optimize energy consumption and user experience using historic data to make assumptions on the available bandwidths along a planned journey. We are able to compute all schedules that lead to Pareto-optimal solutions in quadratic time. Applying our approach to the real-world scenario of train rides provokes several challenges, mainly due to the facts that trains usually do not keep schedules to the split second and that the available bandwidth at one particular client may be significantly smaller during rush hour than during other times of day. We tackle these challenges by employing our own simulation environment to analyze the dependencies on the time of day and to optimize various robustness parameters. In extensive real-world experiments - spending more than 200 hours in local trains - we demonstrate that it is feasible to simultaneously improve the user experience and to save communication energy.