Video through a crystal ball: effect of bandwidth prediction quality on adaptive streaming in mobile environments

Abstract

Mobile environments are characterized by rapidly fluctuating bandwidth and intermittent connectivity. Existing video streaming algorithms can perform poorly in such network conditions because of their reactive adaptation approach. Recent efforts suggest that bitrate adaptation using proactive accurate bandwidth prediction can help improve the quality of experience (QoE) of video streaming. However, highly accurate long-term predictions may be needed in mobile environments and those can be difficult to obtain. In this work, we examine the impact of bandwidth prediction quality on the QoE. We first characterize bandwidth profiles where bandwidth prediction-based adaptation can be useful. We then study the impact of prediction horizon and errors on the performance of Adaptive Bitrate (ABR) streaming. We observe that performance improves as the prediction horizon increases at first and then benefits start to diminish. We demonstrate that with proper error mitigation heuristic, even erroneous predictions can be useful in some scenarios. Finally, we study the role of video system parameters, namely buffer size and bitrate granularity on bandwidth prediction-based adaptation.