Bayesian-based video sharing in mobile social networks
Chenguang Kong, Xiaojun Cao, Ming Liu · 2013
Video sharing is popular and attractive for mobile users. However, the limited resources such as the connection, power and bandwidth, coupled with the social characteristics in mobile social networks pose unique challenges for effective video sharing. Users personal attributes and preferences on the communication pattern or the videos themselves may impede or promote the disperse process of the videos. It is important to take both the network and social behavior factors into consideration to comprehensively analyze the video sharing process. In this work, for the first time, we investigate the factors influencing users decision and model them using the Bayesian technique. The social confidence, interest matching, and resources status are incorporated in the proposed Bayesian-based model to analyze the interactions and influence among social network users. Novel streaming schemes are proposed to provide reliable streaming video transmissions. Our analysis results show that the proposed model is able to help user to establish the most reliable and effective routing paths to obtain the videos.