A Neighbors-Buffering Based Technique to Provide Scalable Distance Learning Service in a Multicast Environment

In this paper, we propose an approach to provide a significantly scalable video-on-demand service mainly designed for multicast environments where users are geographically close to each other and may have access to the same video feature at nearly the same time. Distance learning is a suitable candidate for this approach. The basic idea behind our proposed approach is to advantage of appropriate buffering of participants of a particular session to satisfy new requests willing to join the same session. Our approach repeatedly transmits popular video data on staggered channels. If a request comes in between staggered start times, the user joins to the most recently started multicast session and then requests the missing part from a nearby neighbor. Users must have enough buffer space to buffer data between staggered transmissions. We refer to our proposed architecture as Neighbors-Buffering Based Video-on-Demand (NBB-VoD) architecture. Analytical results show that our proposal achieves a better performance than the already existing system, Unified-VoD, in terms of both scalability and disk-bandwidth requirements.