A cross-layer adaptive handoff algorithm in wireless multimedia environments

Providing multimedia services in wireless networks is concerned about the performance of handoff algorithms because of the irretrievable property of real-time data delivery. To lessen unnecessary handoffs and handoff latencies which can cause media disruption perceived by users, we present in this paper a cross-layer handoff algorithm base on link quality. Neural networks are used to learn the cross-layer correlation between the link quality estimator such as packet success rate and the corresponding context metric indictors, e.g. the transmitting packet length, received signal strength, and signal to noise ratio. Based on a pre-processed learning of link quality profile, our approach makes handoff decisions intelligently and efficiently with the evaluations of link quality instead of the comparisons between relative signal strength. The experiment and simulation results show that the proposed method outperforms RSS-based handoff algorithms in a transmission scenario of VoIP applications.