Understanding the impact of video quality on user engagement

Abstract

As the distribution of the video over the Internet becomes mainstream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video content providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.

Quality of experience and HTTP adaptive streaming: A review of subjective studies

“…one stalling event every 3 s) over irregular video stallings. While isolated stallings up to approximatively 400 ms were found to be acceptable to the average end-users [5,6], many studies agree on the fact that video stalling in HAS should be avoided at all times to improve user's QoE [19][20][21][22][23].…”

Improving QoE prediction in mobile video through machine learning

“…Besides pure video quality modeling, other papers [6], [9], [10] have addressed the problem of user engagement prediction for HTTP video streaming.…”

“…Recent studies show that the occurrence of stalling is far from negligible in operational networks [8], [16], [18], and especially in cellular networks [18], impacting not only the overall user experience, but also user engagement [6], [9]. For these reasons, having a highly accurate model for QoE prediction in mobile video scenarios becomes of capital interest for operators.…”

QoE Analysis of Media Streaming in Wireless Data Networks

“…The QoE metrics include the startup delay, the rebuffering delay, the starvation probability, and the continuous playback interval. Similar metrics has been used in [9] to reveal the interplay between QoE and user satisfaction.…”