A multi-commodity flow problem for fair resource allocation in multi-path video delivery networks

Video streaming services employ the Internet to distribute content to an ever-increasing number of concurrent viewers. The delivery architecture employed by leading video platforms requires players to run a control algorithm dynamically choosing the video bitrate to match the time-varying network bandwidth and avoid playback interruptions due to buffer underruns. Such an algorithm is generally designed to (selfishly) improve the quality individually perceived by users. Consequently, this control architecture leads, in the optimal case, to maximize the average quality perceived collectively by all users and not to a distribution of resources that is fair in terms of user perceived quality. We argue that video service providers should manage their delivery network to address fairness issues to gracefully degrade the perceived quality equally for all users when resources become scarce. Even though the general problem of providing a fair level of perceived quality does not scale with the cumbersome number of concurrent users, this paper shows that the Multi-Commodity Flow Problem (MCFP) optimization framework is a proper and efficient tool to address this open issue. First, we show how to cast the resource allocation problem to an MCFP and then we propose a strategy to make the resulting problem tractable for video distribution platforms serving massive audiences. The performance of the proposed optimal fair resource allocation strategy is assessed using realistic simulations involving thousands of concurrent video sessions on a real network topology by varying both the total load on the network and key system parameters.