The recent popularity of social networking websites have resulted in a greater usage of internet bandwidth for sharing multimedia content through websites such as Facebook and YouTube. Moving large volumes of multi-media data through limited network resources remains a technical challenge to this day. The current state-of-art solution in optimizing cache server utilization depends heavily on efficient caching policies to determine content priority for video streaming. This thesis proposes two models, namely, Advanced Independent Cascade Model (AICM) and Fast Threshold Spread Model (FTSM) to predict the future access pattern of multi-media content based on the social information of its viewers' Online Social Network (OSN). The prediction results are compared and evaluated against...[ Read more ]

The recent popularity of social networking websites have resulted in a greater usage of internet bandwidth for sharing multimedia content through websites such as Facebook and YouTube. Moving large volumes of multi-media data through limited network resources remains a technical challenge to this day. The current state-of-art solution in optimizing cache server utilization depends heavily on efficient caching policies to determine content priority for video streaming. This thesis proposes two models, namely, Advanced Independent Cascade Model (AICM) and Fast Threshold Spread Model (FTSM) to predict the future access pattern of multi-media content based on the social information of its viewers' Online Social Network (OSN). The prediction results are compared and evaluated against ground truth statistics of the respective viral YouTube video. A complexity analysis on the proposed algorithms for large datasets along with the correlation between Facebook social sharing and YouTube global hit count are explored.