Performance of on-off traffic stemming from live adaptive segmented HTTP video streaming

Abstract

Abstract-A large number of live segmented adaptive HTTP video streaming services exist in the Internet today. These quasilive solutions have been shown to scale to a large number of concurrent users, but the characteristic on-off traffic pattern makes TCP behave differently compared to the bulk transfers the protocol is designed for. In this paper, we analyze the TCP performance of such live on-off sources, and we investigate possible improvements in order to increase the resource utilization on the server side. We observe that the problem is the bandwidth wastage because of the synchronization of the on period. We investigate four different techniques to mitigate this problem. We first evaluate the techniques on pure on-off traffic using a fixed quality and then repeat the experiments with quality adaptation.

Adaptive Media Streaming to Mobile Devices: Challenges, Enhancements, and Recommendations

“…Thus, we perform the evaluation in a lab environment with limited resources. The setup is similar to a one-server version of the infrastructure in Figure 1, where clients access the server over a 100 Mbps bottleneck link (we have found similar trends when testing with a 1 Gbps bottleneck link, with only the total number of clients scaled up; see [32] for details). The delays (RTTs) between the clients and the server are normally distributed with an average of 55 ms and a variance of 5 ms; these values correspond well to those observed for ADSL access networks [33].…”

“…The limited congestion window can lead to longer download times, thereby reducing off-periods and resulting in a behavior similar to TCP pacing [36] or server-based traffic shaping [19]. To avoid playout stalls due to congestion window limitation, we chose a congestion window that would allow for a segment to be easily downloaded in one segment duration [32]. The congestion window limit was set to 20 TCP segments, which equals a bandwidth 3 times larger than the average bitrate of the stream (to account for bitrate variance).…”

“…In summary, simple changes to server parameters like TCP congestion control and the client-side request strategy can lead to increased performance in terms of both better liveness and video quality (see [32] for more details).…”

A Real-Time Adaptive Algorithm for Video Streaming over Multiple Wireless Access Networks

“…There is extensive work covering this topic [2]- [6]. The authors in [2] proposed to estimate the bandwidth by a statistical method, and they took both the quality contribution and decoding time of each segment into consideration.…”

“…S. Akhshabi designed an evaluation method in [7] to test the performance of several existing commercial DASH products, such as Smooth Streaming, Netflix, and OSMF. In [6], T. Kupka proposed to evaluate the performance of live DASH under on/off traffic and tested four different methods to improve the performance. In [5], how to reduce unnecessary video quality variations using a probing method to identify the effective available bandwidth was given.…”