Network-based video freeze detection and prediction in HTTP adaptive streaming.

Given the popularity of HTTP adaptive streaming (HAS) technology for media delivery over mobile and fixed networks, the clients Quality of Experience (QoE) for HAS video sessions is of particular interest for network providers and Content Delivery Network (CDN) operators. Despite that, network providers are not able to directly obtain QoE-relevant metrics such as video freezes, initial buffering time, and the frequency of quality switches from the client. This paper proposes a scalable machine learning (ML) based scheme that offline detects and online predicts video freezes using a few features extracted from the network-based monitoring data, i.e., a sequence of HTTP GET requests sent from the video client. We determine the discriminative features for detecting video freezes based on multi-scale windows using the criterion of information gain (IG). Four traditional classifiers are investigated and the C4.5 decision tree is selected because of its simplicity, scalability, accuracy, and interpretability. Our approach for session-based offline freeze detection is evaluated on the Apple HTTP Live Streaming video sessions obtained from a number of operational CDN nodes and on the traces of Microsoft Smooth Streaming video sessions acquired in a controlled lab environment. Experimental results show that, even with the disturbance of user interactivity, an accuracy of about 91% can be obtained for the detection of a video freeze, a long video freeze, and multiple video freezes. The experiments for the online freeze prediction show that more than 30% of the video freezes can be foreseen one segment in advance.