Unveiling Real-Time Stalling Detection for Video Streaming Traffic

Abstract

In the rapidly evolving field of video traffic, ensuring a smooth video streaming experience for users is critical for network operators. Accurately and promptly detecting stalling events, a significant indicator of poor quality of experience, remains challenging due to varying detection time resolutions in existing techniques, which often detect stalls every video chunk, or every five or ten seconds. This paper makes three key contributions. First, we introduce the concept of detection granularities to enable fair performance comparisons and reveal their impact on detection performance from the data sampling perspective. Second, we propose a novel feature extraction approach that captures both packet-level and chunk-level features in a unified sequential manner to effectively detect stalling events. Third, a novel sample reweighting method is proposed to address the detection timeliness problem by focusing more on difficult samples around stalling starting or ending. Experimental results on both video-on-demand and live streaming traces demonstrate that our feature extraction approach achieves an average improvement of 5.3% in f1-score, 4.7% in coverage rate, and reduces stalling response time by 0.4 seconds compared to existing techniques. Additionally, the sample reweighting method further improves the detection sensitivity without compromising f1-scores for all detection techniques.