Watching user generated videos with prefetching

  • Authors:

  • S. Khemmarat;R. Zhou;D. K. Krishnappa;L. Gao;M. Zink

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA;College of Computer Science and Technology, Harbin Engineering University, Harbin, China;Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA;Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA;Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, USA

  • Venue:
  • Image Communication
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Even though user generated video sharing sites are tremendously popular, the experience of the user watching videos is often unsatisfactory. Delays due to buffering before and during a video playback at a client are quite common. In this paper, we present a prefetching approach for user-generated video sharing sites like YouTube. We motivate the need for prefetching by performing a PlanetLab-based measurement demonstrating that video playback on YouTube is often unsatisfactory and introduce a series of prefetching schemes: (1) the conventional caching scheme, which caches all the videos that users have watched, (2) the search result-based prefetching scheme, which prefetches videos that are in the search results of users' search queries, and (3) the recommendation-aware prefetching scheme, which prefetches videos that are in the recommendation lists of the videos that users watch. We evaluate and compare the proposed schemes using user browsing pattern data collected from network measurement. We find that the recommendation-aware prefetching approach can achieve an overall hit ratio of up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates strong potential for improving the playback quality at the client. In addition, we explore the trade-offs and feasibility of implementing recommendation-aware prefetching.