Candid with YouTube: Adaptive Streaming Behavior and Implications on Data Consumption

ABSTRACTYouTube has emerged as the largest player among video streaming services, serving video content for users using DASH. Research studies on various aspects of YouTube, especially its streaming service, abound in the literature. However, these works study YouTube streaming from the periphery, and report results based on their understanding of general DASH recommendations. In this study, we explore in depth YouTube's implementation of the DASH client. We identify important parameters in YouTube's rate adaptation algorithm, and study their roles. In a departure from existing literature, we observe that YouTube opportunistically adapts segment length, in addition to quality level, in response to bandwidth fluctuations. We report that this scheme results in a much lower average data wastage ratio (0.82x10-6), than reported earlier. We also propose an analytical model, augmented with a machine learning based classifier (with average accuracy of 85.75%), to predict data consumption for a playback session in advance.