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As Internet Protocol (IP) multicast allows the efficient use of network bandwidth for multipoint communication, it is expected to be an essential communication type for delivering multimedia services to mobile nodes (MNs). In this study, the authors address an issue of consumer multicast traffic support in a Proxy Mobile IPv6 (PMIPv6) environment wherein network-based mobility management is deployed for MNs. The recently standardised PMIPv6 multicast listener support provides options for deploying multicast listener functions in a PMIPv6 domain, whereas it does not address specific optimisations and efficiency improvements of multicast routing. The authors first review the PMIPv6 multicast listener support and point out the limitations of the current approach. Then, propose an improved multicast handover procedure that optimises multicastgroup management by utilising the context of consumer's MN running multicast applications. The authors develop analytical models to evaluate the proposed multicast handover procedure compared with the base one. From the conducted analysis, it is demonstrated that the proposed multicast handover procedure minimises the service interruption time and prevents the multicast packet loss during handovers. Furthermore, in terms of signalling cost consumption, the proposed multicast handover procedure operates on an equal basis with the base one. [ABSTRACT FROM AUTHOR]

As wireless networks have been an integral part of our daily life, mobility prediction techniques have become one of the main topics in current research efforts. An accurate prediction of the next cell to which the mobile users are going can greatly improve the performance of wireless applications, such as map resource allocation, congestion control, quality of service and mobility management. It has been shown that the Markov predictor is a good mobility predictor in actual wireless local area network environments. However, from the standpoint of conditional entropy, the authors analyse that the Markov predictor has the disadvantage of performing worse when the location history is lacking or an approximate tie has happened. As a consequence, a novel improved Markov predictor is proposed, and simulations are conducted to evaluate the performance of the proposed scheme. The simulation results show that the improved Markov predictor solves not only the disadvantages of Markov predictor due to the lack of location history information, but also the expansion of state space in multiple-order Markov predictors. [ABSTRACT FROM AUTHOR]