Waiting-time Minimization For Video Streaming In UDN With Assisted Renewable Energy Collaboration

Ultra Dense Networks(UDN)is one of the important solutions to solve the explosive growth of network capacity problems in the network,and can improve the comprehensive performance of all levels and the user experience of various mobile services.Energy harvesting(EH)technology refers to equipment that uses energy harvesting devices to collect energy from renewable energy sources such as light energy,solar energy,wind energy,and electromagnetic energy from the environment.The introduction of energy harvesting and energy collaboration technology into the video stream research in UDN can achieve the energy saving purpose of wireless communication systems and solve the problem of minimizing waiting time,which is one of the development trends of green communication in the future.Compared with other online optimization methods,the Lyapunov optimization method has lower computational complexity and wider application range.Convex optimization is an important branch of optimization problems,and most convex optimization problems can be solved by using existing algorithms and computing power.Combining the Lyapunov method with the convex optimization method and introducing it into the video stream problem solving in UDN has research value and exploratory significance.In this paper,the video streaming problem in UDN is deeply studied,the main work is as follows:1.Research the problem of introducing energy harvesting and collaboration technology to minimize the waiting time of video equipment.In this problem,the main source of energy for a small base station is the renewable energy collected by itself and the renewable energy shared by other small base stations.This paper first established a system model and an energy collaboration model between base stations for the research problem.we designed an online algorithm to minimize the average waiting time of mobile users' video equipment by controlling the stability of the virtual queue based on the Lyapunov optimization method.After that,this paper proved the convexity of the sub-optimization problem under a single slot and used convex optimization method to solve it.Finally,it can be seen from the results of the simulation experiment that the proposed algorithm can converge the average waiting time of the video equipment in the system.Under the same simulation conditions,compared with the greedy algorithm,the proposed algorithm can make the average waiting time of the video equipment in the system smaller,and the battery energy required by the base station is smaller.Therefore,this paper conclude that the proposed algorithm can effectively use cooperative energy in this UDN to minimize the average waiting time.The waiting time of the video equipment can be further reduced by selecting the value of the parameter reasonably.2.The collection of renewable energy in the base station may fluctuate greatly due to changes in the surrounding environment,and sometimes cannot meet the working requirements of the base station.Since the problem of multi-base station and multi-device video streaming in UDN requires long-term stable and normal operation of video equipment,this paper have studied the long-term optimization of the average waiting time of system video equipment.By using the Lyapunov optimization method,this paper can get the sub-optimization problem under a single slot.In the simulation,this paper assume that bad conditions cause a certain base station to fail to collect energy normally.At this time,this paper compare the average waiting time of the corresponding equipment of each base station when the proposed algorithm and the greedy algorithm are used.It can be seen from the simulation experiment results that when a certain base station cannot obtain renewable energy,the energy cooperation between the base stations ensures the normal use of the video equipment corresponding to each base station.In summary,the proposed algorithm can provide long-term stable operation guarantee for the system.