YouTube Can Do Better: Getting the Most Out of Video Adaptation

YouTube, as one of the major HTTP Adaptive Streaming video services, accounts for a large fraction of today's Internet traffic. Therefore, it is important to understand how efficiently YouTube uses available network resources. Previous work observed that the YouTube player replaces previously buffered segments with higher quality segments. This is good for the user as it increases the average quality level. However, the lower quality level segments are discarded and their traffic is redundant and therefore wasted. In this paper, we use two independent approaches to evaluate the efficiency of YouTube's quality adaptation algorithm. The first approach performs regression based on previously collected video views from a large experimental data set. In the second approach we formulate a mixed integer linear program and calculate the optimal video quality adaptation. The results show that the simplistic regression approach gives an accurate estimation of the optimal adaptation. Furthermore, the optimization shows that the Quality of Experience (QoE) can be significantly improved compared to the actual average quality level observed in the real-world experiments, demanding for better video quality adaptation mechanisms by YouTube.