Abstract
The generation, processing and distribution of multimedia data from video are increasingly toward the edge of the network with the development of mobile and industrial network. The uncertainty of user behavior and limited system resources have become a major challenge for network video services, for example, the distribution of teaching surveillance video. It is a hot spot to support network video services and content distribution with lower latency and higher bandwidth requirements by using the computing, storage, and network resources at the edge of network. In this paper, we first analyze the challenges which are faced in video distribution based on edge computing; then propose a framework for teaching surveillance video content distribution through the network, storage, and computing capabilities of edge computing; lastly provide an edge caching architecture and a cache update strategy by using a LSTM network. The experimental results demonstrate the proposed framework is more efficient than previous ones.
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