Systematic, large-scale analysis on the feasibility of media prefetching in Online Social Networks

Huge quantities of videos are shared via Online Social Networks (OSN) like Facebook and are watched on mobile devices. Internet connections via cellular networks (UMTS / LTE) require the scarce resources radio bandwidth and battery power. Prefetching of videos in areas of WLAN availability has the potential to reduce the power consumption in comparison to data transmission via cellular networks and prefetching can help to avoid users running into traffic caps of their network providers. Furthermore, startup delays can be reduced. Social networks offer contextual information such as likes and comments as well as social graph information which can potentially be used to predict which content will be consumed in the near future. In this paper, we elaborate possibilities to predict content consumption based on the number of likes, comments and the social graph distance. Our detailed analysis of the media access patterns of more than 700 users in Facebook shows that the media consumption does not solely depend on the number of likes or comments. Users tend to watch videos that are uploaded by close friends and family members. Furthermore, the time a video preview stays in the browser-viewport before being clicked (pre-click delay) can be exploited to decrease startup delays.