Reinforcement learning for dynamic multimedia adaptation
Journal of Network and Computer Applications
Channel Management for Multi-Service Traffic in Cellular Wireless Networks
Wireless Personal Communications: An International Journal
Verifying bandwidth control functions in a real networking
IMCAS'06 Proceedings of the 5th WSEAS international conference on Instrumentation, measurement, circuits and systems
A seamless service management with context-aware handoff scheme in ubiquitous computing environment
APNOMS'06 Proceedings of the 9th Asia-Pacific international conference on Network Operations and Management: management of Convergence Networks and Services
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We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.