Detection and Mitigation of Impairments for Real-Time Multimedia Applications

Measures of Quality of Service (QoS) for multimedia services should focus on phenomena that are observable to the end-user. Metrics such as delay and loss may have little direct meaning to the end-user because knowledge of specific coding and/or adaptive techniques is required to translate delay and loss to the user-perceived performance. Impairment events, as defined in this dissertation, are observable by the end-users independent of coding, adaptive playout or packet loss concealment techniques employed by their multimedia applications. Methods for detecting real-time multimedia (RTM) impairment events from end-to-end measurements are developed here and evaluated using 26 days of PlanetLab measurements collected over nine different Internet paths. Furthermore, methods for detecting impairment-causing network events like route changes and congestion are also developed. The advanced detection techniques developed in this work can be used by applications to detect and match response to network events. The heuristics-based techniques for detecting congestion and route changes were evaluated using PlanetLab measurements. It was found that Congestion events occurred for 6-8 hours during the days on weekdays on two paths. The heuristicsbased route change detection algorithm detected 71% of the visible layer 2 route changes and did not detect the events that occurred too close together in time or the events for which the minimum RTT change was small. A practical modelbased route change detector named the parameter unaware detector (PUD) is also developed in this deissertation because it was expected that model-based detectors would perform better than the heuristics-based detector. Also, the optimal detector named the parameter aware detector (PAD) is developed and is useful because it provides the upper bound on the performance of any detector. The analysis for predicting the performance of PAD is another important contribution of this work. Simulation results prove that the model-based PUD algorithm