Automated QoE evaluation of Dynamic Adaptive Streaming over HTTP

Abstract

Dynamic Adaptive Streaming over HTTP (DASH) is referred to as a multimedia streaming standard to deliver high quality multimedia content over the Internet using conventional HTTP Web servers. As a fundamental feature, it enables automatic switching of quality levels according to network conditions, user requirements, and expectations. Currently, the proposed adaptation schemes for HTTP streaming mostly rely on throughput measurements and/or buffer-related metrics, such as buffer exhaustion and levels. In this paper, we propose to enhance the DASH adaptation logic by feeding it with additional information from our evaluation of the users' perception approximating the userperceived quality of video playback. The proposed model aims at conveniently combining TCP-, buffer-, and media content-related metrics as well as user requirements and expectations to be used as an input for the DASH adaptation logic. Experiments have demonstrated that the chosen model enhances the capability of the adaptation logic to select the optimal video quality level. Finally, we integrated all our findings into a real DASH system with QoE monitoring capabilities.

The Impact of Adaptation Strategies on Perceived Quality of HTTP Adaptive Streaming

“…The authors in [1] have shown that adaptation strategy related parameters (stalling, representation switches) have considerable influence on resulting QoE and more importantly, that they have to be considered on a larger time scale (up to some minutes) than video encoding related parameters (resolution, frame rate, quantization parameter, bit rate) which only influence in the order of a few seconds. In addition to those larger time scales, also impairments, such as stalling and quality switches, have not received much attention in more traditional quality assessment methodologies for subjective testing [12].…”

Impact of media-related SIFs on QoE for H.265/HEVC video streaming

“…Video coding affects the quality perception of video streaming as stated in literature [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. A variety of video coding standards have been analysed in this context, such as H.264/ advanced video coding (AVC) or H.265/HEVC.…”

“…Higher bitrate enables higher degree of user satisfaction [16,34], but also requires higher resolution and more network resources which can be challenging when bandwidth is limited [15,21]. Furthermore, bitrate is related to the transmission efficiency.…”

“…In addition, low frame rate decreases the video quality perception [16,21,22,26,28], while high frame rate increases the quality only to a certain extent. Frame rate depends on many SIFs, such as dynamics of video movement or network conditions [13,14,35] which should be considered in video adaptation [18].…”

“…Definition of ffmpeg parameters and their relation to media-related SIFs are presented in Table II. A total of 27 video sequences were created based on the variation and combination of the ffmpeg parameters, i.e., resolution (858×480, 1280×720, 1280×960), CTU (16,32,64), and CRF (18,28,38).…”