Developing a predictive model of quality of experience for internet video

Abstract

Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement-centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20% improvement in overall user engagement compared to strawman approaches.

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].…”

“…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.…”

Reliable multicast routing for software-defined networks

“…The recovery nodes are chosen randomly in SPT and ST. We change the network size |V |, the number of destinations k, and the number of recovery nodes r. The performance metrics contain 1) total cost (including tree and recovery costs), 2) total retransmitted bytes in networks, and 3) average latency (including retransmissions) of each packet observed by each destination. Packet retransmissions delay the contents received by each destination node and thus tend to deteriorate video quality of experience (QoE) [31]. In our simulation, a link with higher delay and higher loss rate (due to congestion) is assigned a larger cost according to [24].…”

A Hierarchical Classification Model of QoE Influence Factors

“…According to Balachandran et al [2], despite this broad consensus, our understanding of Internet video QoE is limited. The author explained that the reason is that Internet video introduces new effects with respect to both quality and experience.…”