Adaptive Scheme for Caching YouTube Content in a Cellular Network: Machine Learning Approach

Abstract

Content caching at base stations is a promising solution to address the large demands for mobile data services over cellular networks. Content caching is a challenging problem as it requires predicting the future popularity of the content and the operating characteristics of the cellular networks. In this paper, we focus on constructing an algorithm that improves the users’ quality of experience (QoE) and reduces network traffic. The algorithm accounts for users’ behavior and properties of the cellular network (e.g. cache size, bandwidth, and load). The constructed content and network aware adaptive caching scheme uses an extreme-learning machine neural network to estimate the popularity of content, and mixed-integer linear programming to compute where to place the content and select the physical cache sizes in the network. The proposed caching scheme simultaneously performs efficient cache deployment and content caching. Additionally, a simultaneous perturbation stochastic approximation method is developed to reduce the number of neurons in the extreme-learning machine method while ensuring a sufficient predictive performance is maintained. Using real-world data from YouTube and a NS-3 simulator, we demonstrate how the caching scheme improves the QoE of users and network performance compared with industry standard caching schemes.