Research On Heterogeneous Resource Allocation Based On Video Transcoding In Ultra Dense Network

With the popularity and development of the mobile Internet,data traffic in mobile networks is showing a rapid increase.This has put the capacity and processing power of the mobile network to the test.In order to reduce the network load,the ultra-dense network has improved the spectrum utilization rate through cell densification,and optimized the network performance.With the further development of caching and mobile edge computing,small base stations in ultra-dense networks are given caching and computing capabilities,which further enhances the data processing capabilities at the network edge,which is conducive to the carrying,processing,and transmission of video content.,Video content can be stored in advance at the edge of the network,reducing the delay for users to acquire content;on the other hand,real-time transcoding of video content can also be performed at the edge of the network to meet the diverse needs of users for video and enhance users experience.As users continue to increase demand for video services,it is difficult for a single network node to meet new user experience quality requirements.At the same time,the heterogeneous resources distributed in each node need to be comprehensively considered and uniformly allocated in order to maximize the overall network performance.This paper comprehensively considers the cache,computing,and communication resources in ultra-dense networks,and proposes a cooperative transcoding and distribution mechanism based on overlapping clustering and a video cache and transcoding mechanism based on content fragmentation to optimize the network's latency performance.The main work of this article includes:A cooperative transcoding and distribution mechanism based on overlapping clustering is proposed.First,the effects of computing and channel resources on the initial latency of the video are studied.Secondly,a model of topology structure and resource distribution in the network is established.With the goal of minimizing the initial buffer time of the video,the overall delay optimization problem of the network is divided into two sub-problems:grouping and resource allocation.Thirdly,an algorithm combining KM matching and genetic algorithm is proposed to overlap nodes in an ultra-dense network and allocate heterogeneous resources of the grouped base stations.Finally,simulation results show that the proposed algorithm optimizes the user's average initial buffering delay.A video caching and transcoding mechanism based on content fragmentation is proposed.First,consider the different cache preferences of content providers and network operators.Then,a many-to-many matching model between the two is established,and a cache update strategy based on fragmented cache and many-to-many matching is proposed with the goal of minimizing the waiting time of user requests.Finally,it can be seen from the simulation results that the proposed cache and computation joint resource allocation algorithm has better latency performance.