The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Hidden Markov modeling for network communication channels
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Continuous-time hidden Markov models for network performance evaluation
Performance Evaluation
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
An adaptive FEC algorithm using hidden Markov chains
ACM SIGMETRICS Performance Evaluation Review - Special issue on the fifth workshop on MAthematical performance Modeling and Analysis (MAMA 2003)
Modeling frame-level errors in GSM wireless channels
Performance Evaluation - Internet performance symposium (IPS 2002)
On the compromise between burstiness and frequency of events
Performance Evaluation - Performance 2005
Modeling the short-term dynamics of packet losses
ACM SIGMETRICS Performance Evaluation Review
Predicting packet loss statistics with hidden Markov models
ACM SIGMETRICS Performance Evaluation Review
Perceptual QoS assessment technologies for VoIP
IEEE Communications Magazine
A survey of packet loss recovery techniques for streaming audio
IEEE Network: The Magazine of Global Internetworking
Applications of machine learning to performance evaluation
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
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Media streaming applications often try to cope with packet losses using end-to-end recovery mechanisms like FEC. Predicting future losses is critical to choose the proper amount of redundancy needed to recover data. We propose a hierarchical model where the short-term dynamics of losses is driven by 2-state Markov chains while longer-term network changes (e.g., congestion) are modeled by a HMM. Based on this model, we develop two adaptive algorithms that predict future loss statistics and dynamically adjust FEC parameters. First, we predict loss rates and use these estimates to tune redundancy in Reed-Solomon codes. Second, we predict both loss rate and burstiness to select the optimal scheme among a set of parity-based FEC schemes. We perform experiments with packet loss traces to evaluate these algorithms, and compare their performance to standard approaches of FEC selection. Our results show that HMM-based prediction is more effective than other approaches, achieving higher quality improvements with small transmission overhead.