A social-aware video sharing solution using demand prediction of epidemic-based propagation in wireless networks

Abstract

The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user “pull” and “push” sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions.