Quality of service and mobility for the wireless internet
Wireless Networks
The Wireless Hierarchical Token Bucket: A Channel Aware Scheduler for 802.11 Networks
WOWMOM '05 Proceedings of the Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks
Performance Enhancement of Multirate IEEE 802.11 WLANs with Geographically Scattered Stations
IEEE Transactions on Mobile Computing
Context-Aware Multimedia Middleware Solutions for Counteracting IEEE 802.11 Performance Anomaly
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Application-Level middleware to proactively manage handoff in wireless internet multimedia
MMNS'05 Proceedings of the 8th international conference on Management of Multimedia Networks and Services
IEEE Communications Surveys & Tutorials
Mobility support in wireless Internet
IEEE Wireless Communications
IEEE Transactions on Multimedia
Mobile and wireless Internet services: putting the pieces together
IEEE Communications Magazine
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The need for application-level context visibility to properly perform streaming service management in wired-wireless integrated networks is widely recognized. In particular, the paper claims the need for full application-level awareness of context data about the IEEE 802.11 performance anomaly, i.e., when even a single node located at the borders of the coverage area of a Wi-Fi access point produces a relevant degradation in the connectivity quality of all other nodes in the area. We propose a middleware that on the one hand portably predicts and detects anomaly situations via decentralized and lightweight client-side mechanisms and, on the other hand, exploits anomaly awareness to promptly react with application-level management operations (streaming quality downscaling and traffic shaping). In particular, the paper focuses on how our middleware performs anomaly-driven quality downscaling both to preserve the goodput at nodes in well-covered areas and to minimize quality degradations at the clients generating the anomaly. The reported experimental results point out how anomaly prediction/detection can relevantly improve the effectiveness of streaming downscaling, thus allowing to maintain acceptable service quality notwithstanding Wi-Fi anomaly occurrences.