Research On WiFi Video Multicast Transmission Based On Mobile Edge Computing

In recent years,with the development of the mobile Internet and the popularization of intelligent mobile devices,new and eye-catching applications in all walks of life have been overwhelmingly developed,and mobile data traffic led by video services has exploded.In order to meet user's low latency requirements for high-definition video,Mobile Edge Computing(MEC)with caching and computing capabilities has being researched.It enhances the mobile network edge by deploying high-performance MEC servers near mobile users,which in turn reduces the time that user requests are responded to.Therefore,in the MEC network,how to effectively use cache resources and computing resources has become a key issue.For user-intensive scenarios,the same video is more likely to be requested by multiple users at the same time.In order to improve resource utilization and use limited resources to provide video services for more users,this thesis focuses on video caching,transcoding decisions,and multicast transmission.The main work is as follows:First of al,this thesis has conducted a full investigation on the network architecture and related technologies of MEC,such as caching technology,transcoding technology,computing offloading and location awareness technology,as well as the main applications of MEC in smart medical care,Internet of Vehicles and other aspects.The development status of the video streaming service in the MEC architecture is expounded,and the existing problems and challenges in Wi Fi multicast and the solutions are accordingly summarized.Secondly,from the perspective of the system,considering the MEC cache video energy consumption,transcoding energy consumption,and the transmission energy required to obtain video from the backhaul,this thesis combines the MEC cache and transcoding scheduling strategy and adds the multicast transmission method to achieve The overall energy efficiency of the system.In order to make the NP-hard problem easy to solve,this thesis uses a heuristic scheme based on Simulated Annealing(SA)algorithm to solve the optimization problem iteratively.Simulation results show that the scheme has good performance under different caching strategies.Finally,from the user level,taking into account the user's low latency requirements for delaysensitive video applications,this thesis analyzes the user's delay by compressively considering the transmission delay of obtaining uncached video from the source server,wireless transmission delay,and MEC transcoding time.The optimization goal of minimizing video transmission delay designs transcoding decision and power alocation scheme,which has been established under the condition of MEC transcoding restriction and power limitation.The performance of the algorithm is analyzed through simulation,genetic algorithm(Genetic Algorithm)is used to solve the transcoding decision and the traditional water injection algorithm is used to optimize the power distribution.