Adaptive video pacing method based on the prediction of stochastic TCP throughput

Many of the video streaming services on the Internet and mobile networks use HTTP/TCP for video transport. Video streaming clients are generally designed to download a sufficient amount of data to avoid playback interruption caused by the lower TCP throughput. Storing more video data than necessary may generate a large amount of wasted traffic when users quit playing video before all of the downloaded video data are played. To prevent downloading unnecessary video data, HTTP downloading with just-in-time delivery features, called video pacing, is focused on. However, video pacing may cause playback discontinuity, resulting from a decrease in the playout buffer size. This is because conventional video pacing methods control the playout buffer size without considering the TCP throughput fluctuation. In this paper, we propose an adaptive video pacing method that enables both reducing unnecessary video traffic and maintaining user-perceived quality. The proposed method has two unique video-pacing functionalities, namely, predicting the stochastic diffusion of TCP throughput and controlling the playout buffer size by taking into account the future throughput. Experimental results from simulating mobile networks show that the proposed method can decrease the average playout buffer size in stable environments by up to 42.62% compared with the conventional methods. Moreover, the frequency of the playback discontinuity of the proposed method is shown to be up to 34.1% less than that of the conventional methods in unstable environments.