A Queue-Based Bandwidth Allocation Method for Streaming Media Servers in M-Learning VoD Systems

Nowdays, VoD (video-on-demand) has become a wide-used technology in m-learning. In m-learning VoD systems, we need to allocate appropriate bandwidth for streaming media servers with the aim of optimizing the user experience and reducing the service cost. In this paper, a queue-based bandwidth allocation method for streaming media servers in m-learning VoD system is proposed. Firstly, it analyzes the user historical learning logs to mine the user behavior characteristics. Secondly, it utilizes the queueing theory to establish a bandwidth resource allocation model for streaming media servers. Thirdly, it predicts the user arrival rate in real-time, allocates appropriate bandwidth resource dynamically by the bandwidth resource allocation model, so as to solve the bandwidth resource allocation irrationality problem. Finally, the simulation results have proved the correctness and effectiveness of the proposed bandwidth resource allocation method, which can improve the bandwidth resource utilization and reduce the service rejection rate.