ABSTRACT:
YouTube has developed as the largest player among video streaming services, serving QoE-streamlined substance for clients utilizing 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 a report results about based on their understanding of general DASH proposals. In this study, we investigate inside and out YouTube’s implementation of the DASH customer. We recognize essential parameters in YouTube’s rate adaption algorithm and concentrate their roles. In a departure from the existing literature, we watch that YouTube opportunistically adapts segment length, notwithstanding quality level, in response to bandwidth fluctuations. We report that this plan brings about a much lower average information wastage ratio (0.82×10−6) than detailed before. We additionally propose an analytical model, increased with a machine learning based classier (with the average accuracy ) to predict data consumption for a playback session in advance.
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