LAUSR

Video delivery networks (VDNs) are one of the key enablers for alleviating contradiction between demands increasing for online video and response time of users accessing video content. However, existed works mainly adopt either proactive or reactive delivery scheme, which leads to the problems of backbone traffic overloaded and delays increased. In these works, the impact of performance indicators, such as bandwidth, delay, and personalized demand on delivery cost are not comprehensively taken into account. To overcome these problems, we propose a cost optimization for coupled video delivery model (CO-CVDM), which combines coupled proactive delivery and coupled reactive delivery method to minimize delivery cost in terms of bandwidth consumption, delay performance, personalized demand and construct a multicast delivery tree for the video delivery. The coupled delivery cost minimization problem under constraint conditions is formulated. Since this problem is NP-hard and is prohibitively difficult to solve, we develop a heuristic multicast delivery tree (HMDT) algorithm to provide more optimized approximate cost. Based on this, we extend CO-CVDM to distribute video in a dynamic scenario. Specifically, with dynamic change of user’s requirement for video, the process of video delivery is truncated into a sequence of static graphs and the change of interest attenuation is modeled with human forgetting curve. Finally, the numerical simulations are conducted to validate the advantage of CO-CVDM in terms of optimization delivery cost, and the delivery cost is inversely proportional to the attenuation of user interest.