SkipStreaming: Pinpointing User-Perceived Redundancy in Correlated Web Video Streaming through the Lens of Scenes

摘要

When streaming over the web, correlated videos (e.g., a series of TV episodes) appear to bear considerable redundant clips, mostly included in the intros, outros, recaps, and commercial breaks, leading to a waste of network traffic and playback time. Mainstream video content providers have taken various measures to identify these clips, but often result in unexpected and undesirable user experiences. In this paper, we conduct a large-scale, crowdsourced study to demystify the root causes of poor experiences. Driven by the findings, we propose to reconsider the problem from a novel perspective of scenes without going through the excessive video frames, which pays special attention to how the contents of correlated videos are organized during video production. To enable this idea, we design efficient approaches to the separation of video scenes and the identification of visual redundancy. We build an open-source system to embody our design, which achieves fast (e.g., taking ~38 seconds to process a 45-minute video using a common commodity server) and accurate (incurring only 770-ms deviation on average) redundancy recognition on representative workloads.