Multi-neural network based tiled 360°video caching with Mobile Edge Computing

Abstract

It is challenging to stream 360° videos over the mobile network for its stringent latency and high bandwidth requirements. Although edge-based viewport adaptive tiled 360° video streaming solutions alleviate the bandwidth demand, the backhaul congestion and low latency concern remain persistent when data is served from the Content Delivery Network over the Internet. Edge caching can help mitigate these issues by storing the content at the edge of the cellular networks on the base station. However, caching 360° videos is challenging because of the large file size, which is further convoluted by tile selection in caching decisions. In this work, we propose a Mobile Edge Computing (MEC) based tiled 360° caching solution that uses Long–Short-Term-Memory (LSTM) and Convolutional Neural Network (CNN) in conjunction to address the challenges associated with 360° video caching. Specifically, the LSTM model predicts the future popularity of the videos, assisting in cache replacement decisions. For the selected videos, the CNN model, which is trained using the saliency map of the video, identifies the most engaging tiles in the videos for caching using the video content itself. The caching of tiles instead of the whole 360° videos improves the caching efficiency of the resource-constrained MEC server. The LSTM model is optimized based on the loss value of different hyperparameters, and AUROC (Ares Under ROC Curve) is used to evaluate the accuracy of the CNN model. Both the models produce highly accurate results. The results from extensive simulations show that the proposed solution significantly outperforms the existing methods. It improves the cache hit rate by at least 10% and reduces the backhaul usage by at least 35% with significant improvement in end-to-end latency, which is crucial for the quality of experience in 360° video streaming.