No 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 showing that video playbacks of videos on YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. 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 up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

No 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 showing that video playbacks of videos on YouTube is often unsatisfactory and introduce a series of prefetching schemes: the conventional caching scheme, the search result-based prefetching scheme, and the recommendation-aware prefetching scheme. 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 up to 81%, while the hit ratio achieved by the caching scheme can only reach 40%. Thus, the recommendation-aware prefetching approach demonstrates a strong potential for improving the playback quality at the client. We also explore the trade-offs and feasibility of implementing recommendation-aware prefetching.

No abstract