Assessing effect sizes of influence factors towards a QoE model for HTTP adaptive streaming

Abstract

HTTP Adaptive Streaming (HAS) is employed by more and more video streaming services in the Internet. It allows to adapt the downloaded video quality to the current network conditions, and thus, avoids stalling (i.e., playback interruptions) to the greatest possible extend. The adaptation of video streams is done by switching between different quality representation levels, which influences the user perceived quality of the video stream. In this work, the influence of several adaptation parameters, namely, switch amplitude (i.e., quality level difference), switching frequency, and recency effects, on Quality of Experience (QoE) is investigated. Therefore, crowdsourcing experiments were conducted in order to collect subjective ratings for different adaptation-related test conditions. The results of these subjective studies indicate the influence of the adaptation parameters, and based on these findings a simplified QoE model for HAS is presented, which only relies on the switch amplitude and the playback time of each layer.

On subjective quality assessment of adaptive video streaming via crowdsourcing and laboratory based experiments

“…The results indicated that the delivered representation bitrate and the number of stalls were the main influence factors on the QoE. Finally, [8,14,33] presented surveys on the studies related to various influence factors of QoE in HTTP adaptive streaming.…”

“…Of the related works mentioned above, the subjective experiments in [14,28] were conducted by crowdsourcing. In [14], authors analyse the effect of switch amplitude (i.e., quality level difference), switching frequency, and recency effects on HAS Quality of Experience (QoE) while in [28], authors analyse the effect of average representation bitrate (i.e., media throughput at the client), average startup time (or startup delay), and average number of stalls on existing DASH-based Web clients.…”

Improving QoE prediction in mobile video through machine learning

“…The problem of QoE assessment in HTTP video streaming is already well-known and well studied, and different QoE models for video streaming have been proposed in the past [7], [10], [12], [13], [15], [21], [23]- [25]. Today it is well accepted that stalling (i.e., stops of the video playback) and initial delay on the video playback are the most relevant KPIs for video streaming QoE [12]- [14], [23].…”

“…Today it is well accepted that stalling (i.e., stops of the video playback) and initial delay on the video playback are the most relevant KPIs for video streaming QoE [12]- [14], [23]. Quality switches have also a relevant impact on QoE when considering adaptive video streaming technology [15]; however, in [11] we recently found that QoE for video streaming in modern smartphones is actually slightly impaired by resolution switches, mainly due to the screen size of such devices. Recent studies [21] show that the position of stallings and their length have a relevant impact on QoE, but do not attempt to use such metrics to improve QoE predictions.…”

“…There are different tools [5], [6], [19] which are capable of monitoring application-layer metrics which are highly correlated to QoE in video streaming services. Buffering events or stallings, video quality/resolution switches and initial playback delay are accepted today as the key application-layer metrics which can be used to predict the QoE undergone by the video watcher, using different models proposed and investigated in the literature [7], [10], [12], [13], [15], [21], [23]- [25]. Out of these metrics, stalling is the paramount one, specially when it comes to mobile video watched in small end-devices such as smartphones; in fact, in [11] we show that QoE for video streaming in modern smartphones is actually slightly impaired by video resolution changes.…”