Suffering from buffering? Detecting QoE impairments in live video streams

Abstract

Fueled by increasing network bandwidth and decreasing costs, the popularity of over-the-top large-scale live video streaming has dramatically increased over the last few years. In this paper, we present a measurement study of adaptive bitrate video streaming for a large-scale live event. Using server-side logs from a commercial content delivery network, we study live video delivery for the annual Academy Awards event that was streamed by hundreds of thousands of viewers in the United States. We analyze the relationship between Quality-of-Experience (QoE) and user engagement. We first study the impact of buffering, average bitrate, and bitrate fluctuations on user engagement. To account for interdependencies among QoE metrics and other confounding factors, we use quasi-experiments to quantify the causal impact of different QoE metrics on user engagement. We further design and implement a Principal Component Analysis (PCA) based technique to detect live video QoE impairments in real-time. We then use Hampel filters to detect QoE impairments and report 92% accuracy with 20% improvement in true positive rate as compared to baselines. Our approach allows content providers to detect and mitigate QoE impairments on the fly instead of relying on post-hoc analysis.