Proactive caching of online video by mining mainstream media

Abstract

Online video sharing is becoming more and more popular and demands a large amount of network resources. Caching content has been an effective method for providing better quality of service for video applications. In the age of social network, information sharing and consumption has significantly changed. More and more viewers of online videos are influenced by the trends in social and mainstream media. Given the rich information about the trends in the media, we propose a cross-platform, proactive video caching scheme in this paper. We employ a combination of the topic modeling tool Latent Dirichlet Allocation (LDA) and frequent pattern mining algorithm Apriori to effectively detect and connect trending topics from mainstream media to online videos. Furthermore, we design a reputation-based video-ranking algorithm to select candidates for caching at geographically relevant proxy nodes to reduce the delay and improve the overall traffic in the network. Simulation results show that our cross-domain proactive caching method performs significantly better than classical caching methods that are based on the historical popularity.