An integrated metric for video QoS

to obtain cost-effective QoS. In this paper, we address the issues in designing metrics that are important in evaluating the Quality of Service(QoS) In this paper, we address the issues in designing metrics that are important in evaluating the QoS of video transmission. There has been little work in det ermining effective metrics of QoS for video transmission that characterize both cost (revenue generated or service demand) and guaranteed service. The metrics of analysis and comparison for video transmission must be determined as an end-to-end measure of QoS from video server to end-user(s). By developing these metrics, we hope to enhance the client, server and networking components of a system with monitoring capabilities to measure and evaluate video characterizations. This paper is organized as follows. In Section 2, we discuss a workload model for developing and understanding QoS metrics. Section 3 presents empirical studies and experimental justification for the metric selection based on the three systems VOSAIC, hierarchical VOD and the remote VCR systems. Section 4 proposes a new integrated metric for measuring video QoS and the analytical framework to express the tradeoffs. We also propose a metric-based QoS architecture along with negotiation and reward protocols. In Section 5 we discuss related work and conclude with future research directions in Section 6. of video transmission. We propose a new metric for video QoS called the weighted cost-satisfaction ratio based on requirements from two perspectives: the user and the service provider. To understand real video workload environments and user behavioral patterns, we obtained and analyzed empirical results from the VOSAIC (video-over-the-Web) system, a hierarchical video-on-demand (VOD) system and a remote VCR system. Based on these results, we define parameters of resource consumption (storage and network bandwidth etc.) and user satisfaction (jitter, syncbronization skew) and derive analytical interrelationships among the metric parameters. We also draw an economic relationship between the user-satisfaction and resource consumption factors to solve metric optimization relations.