Online Detection of Stalling and Scrubbing in Adaptive Video Streaming

Abstract

Whether it is for network engineering or business intelligence insight purposes, it is crucial for an Internet Service Provider (ISP) to infer the Quality of Experience (QoE) perceived by the end user during a video streaming session. Specifically, it is important to detect video stalls as soon as they occur, to rapidly take counter-measures such as re-allocating resources more fairly among users. Video stalls fall into two different classes: (i) those caused by poor network conditions and (ii) those caused directly by the user when scrubbing or dragging the video playback forwards or backwards. However, only the former type of stalls degrade the QoE perceived by the end user. Therefore, in this paper we propose a technique to detect and classify stall events by observing the packets associated to a streaming session. We solve a least squares problem to minimize the distance between the estimated chunk's bitrate and the potential bitrate sequence that a plausible playback buffer dynamics would produce. This amounts to finding the maximally likely state sequence for a properly defined Hidden Markov Model. We propose two polynomial dynamic programming algorithms, one of which running in online fashion, computing the exact solution in the ideal case of complete and exact measurement set. We claim that our method is also applicable in an encrypted scenario, since it is robust with respect to the estimation error of a number of parameters, as we show via simulations.

QoE‐aware traffic monitoring based on user behavior in video streaming services

“…Recent works emphasize analyzing user behavior. Maggi et al 31 proposed an algorithm classifying stall events as being occurred by users' interaction or bad network conditions. The authors in Reference 9 investigated the extent to which the context data on a different level (content, playback, user, system/device) could improve QoE estimation for adaptive streaming.…”