Research On Video Transmission Mechanisms Optimization In Edge Network

With the rapid development of mobile networks and wireless communication tech-nologies,more and more video-based applications appear in people's daily lives,and video applications occupy most of the communication traffic on the Internet.Huge video traffic and increasing video service quality requirements have brought severe challenges to the traditional cloud services.On the one hand,the load and bandwidth cost of the cloud video server is very large,and the end-to-end transmission of high-quality video brings heavy backhaul transmission pressure to the entire network.;on the other hand,due to the separation design of video service and mobile network,it is difficult for video service to perceive the real-time state change of wireless network,and the video transmission is difficult to achieve the best effect.The edge network can provide users with more flexible and efficient services by sinking part of the resources and functions originally located in the cloud to the edge nodes of the network,thereby alleviating the above-mentioned problems.The storage capacity and computing capacity deployed on the edge nodes can well improve the user's video service experience.Among them,the use of the storage ca-pacity of edge nodes for video caching can not only efficiently meet users' repeated requests for popular video contents during the video distribution process and reduce the redundant traffic in the network,but also shorten the delay of video services and provide users a better video experience;Using the computing power of the edge nodes to per-form video transcoding could have a faster response speed than transcoding in the cloud.At the same time,the edge nodes can capture the changes of users' wireless channels in time and provide users with the most suitable set of transcoding rates.Considering the importance of caching strategy and transcoding strategy and the shortcomings of cur-rent research in some scenarios,this paper focuses on the caching placement strategy of SVC videos and the transcoding strategy of live videos in the edge network.This paper mainly includes the following parts:(1)For SVC videos,this paper focuses on the layered characteristics of SVC videos in transmission,storage,and video bit rate.We optimizes the transmission of SVC videos by designing a joint caching placement and bitrate selection strategy.Different from caching data and ordinary video,the channel distribution of users will signifi-cantly affect the caching effectiveness of different layers of SVC videos.Based on it,we consider the user's receiving capability and quality selection strategy when caching different video layers.This could avoid caching some video layers which cannot be received smoothly by users.The problem is modeled as a mixed-integer continuous programming problem.We first convert it into a discrete optimization problem with lower complexity through difference,and then propose a heuristic two-step dynamic programming algorithm to solve it.The results of simulations show that our proposed joint strategy can improve the average QoE of users more effectively than other com-bined strategies.(2)For live videos,this paper focuses on the impact of different transcoding tar-get bitrate sets on the user's video experience,and we optimizes it by designing joint dynamic transcoding and resource allocation strategy.The transcoding strategy of pre-fixed target rates cannot adapt well to the dynamics of mobile users.Also,these strate-gies cannot provide users with the best bitrate selection,which results in a waste of computing resources.To solve this problem,we consider designing a more fine-grained dynamic transcoding strategy,when we also consider the allocation of computing re-sources and transmission power on edge nodes.The problem is constructed as a mixed integer nonlinear programming problem.Through mathematical analysis,we found that in the case of fixed user groups the original question can be simplified.Based on it,we design dynamic transcoding based on user clustering as well as computational resource and transmission power allocation algorithms.After lots of experimental simulations,we confirmed that the proposed joint strategy has significant advantages in improving the total QoE of users.