Adaptive optimisation of a resource allocation algorithm for secure video transmission in 5G networks

This paper presents a novel optimisation model Tasmanian devil whale optimisation (TDWO) for secure transmission of educational video using fifth-generation (5G) cellular networks. At first, the input educational videos taken from the database is transmitted via 5G networks. Then, the allocation of resource is performed using the designed TDWO model by considering different fitness parameters, like data rate, achievable data rate, and quality of experience (QoE). Here, the deep learning model, deep convolutional neural networks (DCNN) is utilised for the prediction of QoE for resource allocation. Moreover, the resource allocation performance of the TDWO is validated by comparing with other resource allocation schemes. Here, the TDWO algorithmic approach achieved superior performance with throughput of 25.557Mbps accuracy of 91.43%, bit error rate (BER) of 0.021, QoE of 18.332, and fitness of 0.013.