Research On Communication Quality Evaluation Model Of Mobile Communication Networks And QOS Of Streaming Media Over Mobile Communication Network

With the rapid development of mobile communications technology and telecommunications market's continuous opening up, the network has been expanding and the number of users increasing dramatically. As the user's requirement of quality of experience raised, major telecom operators are facing unprecedented challenges in this competition. The quality of network recieved more and more attention as the basis for providing quality services to customers. But the current evaluation methods emphasis on traffic statistics, data analysis of other voice switch rate, call blocking, traffic flow by the switches and base stations lacking of reasonable evaluation of the users'perception.This paper studies on the technical characteristics of the network, analysis the core indicators of network quality based on the network KPI, perceives from the user experiment point of view, suggests a top-down hierarchical subdivision indicators definition method to establish a scientific system to evaluate network performance, and gives the concrete definition of key technical indicators based on GPRS network. And at last, the paper gives an example to evaluation the model based on fuzzy evaluation method. It provides a new perspective to evaluate quality of mobile networks.Besides, this paper also researches on the key technology of QoS of streaming media over mobile communication network. Streaming media services over mobile communication network have become one of the hottest research fields. However, due to the features of the mobile devices and the mobile communication network, on which it is hard to use the traditional streaming OoS technology to improve mobile streaming QoS. We propose an end-to-end QoS architecture for Streaming media services over mobile communication network. We discuses a rate-based TCP-friendly congestion control mechanism-TFRC, studies its basic structure and some key details. Due to its control scheme judge all packet loss as congestion loss, we propose a more effective control scheme that we adopt different control mode according to loss type in order to improve its performance. Also we propose a buffer scheme on the client adapt sending rate and client-side and that significantly improve QoS of streaming media services in wireless environment. At last, we make simulation on ns verifies the new algorithms.