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A decision-theoretic approach for quality-of-experience measurement and prediction.
| Content Provider | CiteSeerX |
|---|---|
| Author | Mitra, Karan Zaslavsky, Arkady |
| Abstract | This paper presents a pioneering context-aware approach for quality of experience (QoE) measurement and prediction. The proposed approach incorporates an intuitive context-aware framework and decision theory. It is capable of incor-porating several QoE related classes and context information to correctly measure and predict the overall QoE on a single scale. Our approach can be used in measuring and predicting QoE in both lab and living-lab settings based on user, device and network related context parameters. The predicted QoE can be beneficial for network operators to minimize network churn and can help application developers to build smart user-centric applications. We perform extensive experimentation and the results validate our approach. Index Terms — Bayesian network, context-awareness, decision theory, quality of experience (QoE), quality of ser-vice (QoS) 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Decision-theoretic Approach Quality-of-experience Measurement Prediction Decision Theory Incor-porating Several Qoe Related Class Network Related Context Parameter Smart User-centric Application Network Churn Intuitive Context-aware Framework Index Term Bayesian Network Context Information Application Developer Overall Qoe Network Operator Single Scale Living-lab Setting Pioneering Context-aware Approach Extensive Experimentation Predicted Qoe |
| Content Type | Text |
| Resource Type | Article |
