Research On User Experience Optimization Strategy Of DASH Services In LTE Network

In recent years,with the development of Internet technologies and evolution of mobile networks,mobile video services have gradually gained popularity.Based on HTTP protocol,DASH(Dynamic Adaptive Streaming over HTTP)is a new kind of streaming media technology.It is flexible to deploy and easy to traverse firewalls.At the same time,Terminals with DASH have the function of rate adaptation,which enables users of DASH service to dynamically select the most appropriate video version to download in consideration of their own situation.With the mentioned benefits of DASH technology,DASH services have been rapidly and widely promoted by the market.However,due to the limited spectrum resources and unstable transmission channel of LTE network,when DASH meets LTE network,the transmission of DASH service is facing a few challenges.This thesis set out from the perspective of QoE(quality of experience),and proposed some meaningful solutions corresponding to existing challenges from both network side and user side in order to enhance quality of user experience.From the perspective of network,we designed an QoE-aware and energy-efficient resource scheduling scheme for the transmission of DASH services.Generally,traditional resource scheduling schemes of LTE protocols mainly consider QoS metrics like end-to-end delay or throughput,which are not very suitable for DASH services that attach more importance to QoE metrics.Furthermore,mobile devices would consume a large amount of energy while downloading DASH video data in the mobile network,which seriously affects the standby time of mobile devices and brings inconvenience to users.Therefore,considering both QoE metrics of users and energy consumption of terminals,we designed a real-time resource scheduling algorithm by modeling users' buffers and energy consumption of terminals and introducing Lyapunov optimization system.The simulation results have demonstrated that our proposed algorithms can reduce stalling time by around 10%.Also,energy consumption of terminals can be reduced compared with other algorithms.From the perspective of user,we proposed a novel rate adaptation scheme based on identification of network state.In the mobile network,wireless channel experiences inherent unstableness due to effects such as fading,interference,and noise.What's more,DASH services may suffer from network congestions during the transmission,which would result in the fluctuation of wired network.Generally,existing rate adaptation schemes of DASH mainly focus on unstableness of wireless channel,but pay little attention to wired link congestions.Therefore,in our proposed rate adaptation scheme,we exploit the benefits of server-and-network-assisted DASH(SAND)technology to obtain some real-time network-running data,such as TCP congestion window value.Further more,we ultilize machine learning models to analyse and process these data and identify the current state of network.Based on the results of recognition,we will adjust upcoming video versions according to instant throughput if there exist congestions in wired network.If not,we will determine next-moment video versions according to average throughput.Compared with traditional algorithms,our proposed algorithm can adjust video versions timely in network congestion scenarios,which in turn help to avoid interruption events of video playback and improve average video rate.