Research On Content Distribution Mechanism In Edge Computing Network For Video Streaming

With the rapid development of mobile Internet technology,mobile data traffic has experienced explosive growth.Particularly,mobile video service has been accounting for a growing proportion of mobile Internet services,which is considered to be a typical business scene of the eMBB in 5G network.The growth of video traffic has brought huge challenges for mobile network,requiring mobile network to provide higher data transmission rate and lower network delay to guarantee users better quality of experience(QoE).In order to deal with the huge challenges faced by mobile network in the future,adaptive bit rate(ABR)technology and edge computing(EC)are proposed to provide users with high experience quality,low latency and high bandwidth in diverse video services.It is of great significance to combine ABR and EC to optimize video content distribution for improving network performance and user QoE.Although there has been a lot of work on the joint research of EC and ABR technology in the field of academia and industry,it is still based on the traditional network architecture and cannot solve the inherent defects brought by the traditional TCP/IP network architecture.Furthermore,for the optimization of video content distribution,not only the reasonable placement of video content and the process strategy of video request,but also the effective forwarding and delivery mechanism between users and services will affect the performance of network and the video viewing experience of users.In this paper,we study a novel network ar-chitecture,which is known as information centric networking(ICN).Based on the integration of EC and ICN,we designed a novel video distribution system architecture and new structures of network nodes.Based on the proposed video distribution system architecture in edge computing network,we further designed a novel mechanism of content processing and packet forwarding.Then we take the overall performance of both network and user side into consideration to optimize the video content distribution process.We designed a cost-efficient and QoE-guaranteed content scheduling policy to implement video content placement and processing,as well as a quality of service(QoS)optimized routing and forwarding strategy to enable forwarding delivery of video request and content.Finally,we introduced simulated annealing algorithm(SA)and genetic algorithm(GA)to solve the above optimization problems.