Scalable Network-Based Video-Freeze Detection for HTTP Adaptive Streaming

HTTP adaptive streaming (HAS) has become a key video delivery technology for mobile and fixed networks. Internet service providers and CDN (content delivery network) providers are interested in monitoring the clients Quality of Experience (QoE) for HAS sessions from the network side. In our previous work, we designed a HAS QoE monitoring system based on the sequence of HTTP GET requests collected at the CDN nodes. The system relies on a technique called session reconstruction to retrieve the major QoE parameters without modifying HAS clients. However, session reconstruction is computationally intensive and requires manual configuration of reconstruction rules. To overcome the limitations of session reconstruction, this paper proposes a scalable machine learning (ML) based scheme that detects video freezes using a few highlevel features extracted from video sessions. We determine the discriminative features for session representation and assess five potential classifiers. We select the C4.5 decision tree as classifier because of its simplicity, scalability, accuracy, and explainability. The assessment is tested on traces of Apple HTTP Live Streaming video sessions obtained from a number of operational CDN nodes and on traces of Microsoft Smooth Streaming video sessions acquired in a controlled lab environment. Experimental results show that an accuracy of about 98%, 98%, and 90% can be obtained for the detection of a video freeze, a long video freeze, and multiple video freezes, respectively. Excluding log parsing, the computational cost of the proposed video-freeze detection is 33 times smaller than for session reconstruction.