vFetch: Video prefetching using pseudo subscriptions and user channel affinity in YouTube

Abstract

Video streaming is responsible for the largest portion of traffic in fixed and mobile networks. Yet, forecasts expect this amount to grow further. Especially for mobile devices connected to cellular networks, high QoE video streaming can be a challenge as the user data volume is metered and eventually limited. Also, the connection quality may vary severely. Prefetching videos is an approach to mitigate this issue. Here, videos that the user is likely to watch in advance are prefetched on the user's smartphone, e.g., while he is connected to WiFi. However, this approach can only be efficient if only the videos that are interesting for the respective user are prefetched. This constitutes a major estimation and prediction challenge. To this end, this paper presents three contributions: First, a user study over multiple months that draws valuable insights on the user video request behavior. Second, we propose a novel privacy-preserving prefetching framework denoted vFetch that prefetches videos based, e.g., on the user's affinity of YouTube channels. Third, a trace-based evaluation and parameter study that demonstrates vFetch's efficiency with a hit rate of ∼50% for a 50 GB cache.