A mobile learning system for supporting heterogeneous clients based on P2P live streaming

Along with the fusion of mobile communication technology and embedded mobile devices, mobile learning (m-learning) has become an important and novel teaching method. A major challenge in designing such a system is how to simultaneously provide large-scale and robust interactive media for all kinds of clients, including the PCs, IPTVs, smart phones, and tablets. In this paper, we devise a two-tier system architecture based on service overlay network (SON) and tree-based peer-to-peer (P2P) live streaming network. Especially, we present a priority-based P2P application layer multicast (PALM) algorithm, which takes some heterogeneity features of various clients into consideration, such as type, bandwidth capacity and historical online runtime, to construct a stable and effective P2P network for supporting heterogeneous clients with enhanced stable performance. Our simulation and experimental results show that PALM can simultaneously provide scalable and robust live interactive streaming media for large-scale heterogeneous clients and it can gain a good load balancing performance as well.

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