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

Abstract

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.

A highly-reliable buffer strategy based on long-term throughput prediction for mobile video streaming

“…In [8], authors have also defined the buffering strategy for YouTube called "block sending algorithm," which is likely to the long ON-OFF cycle strategy. In [9,10], authors have also defined the short/long ON-OFF cycle strategy as "Zippy pacing" / "Sawtooth pacing," respectively.…”

“…Since these basic strategies did not consider network quality degradation, Satoda et al have proposed an adaptive buffer strategy based on throughput prediction in [10]. Their goal is to reduce wasted video traffic which is generated by quitting video playback.…”

“…Though our primal goal is to avoid video playback interruption due to the network quality degradation, especially on subway, similar to [10], we also aim to reduce wasted mobile traffic which is generated by quitting video playback.…”

“…Note that the original DASH-JS adopts the long ON-OFF cycle strategy, but the buffer strategy can easily switch to the short ON-OFF cycle strategy by changing maximum and /or minimum buffer size. We also implemented Satoda method [10] into this application to compare the performance.…”

Analysis and modelling of traffic produced by adaptive HTTP-based video

“…We transmitted several video files, acquired from [56] and presented in Table 2, through the simulation environment with variable network bandwidth and packet delays. Using the edge router with installed capturing software, based on Tcpdump and Libcap [57], we obtained five aggregated traffic traces. The length of each trace was trimmed to 10 minutes.…”