Abstract
In this article, we propose content-centric, in-network content caching and placement approaches that leverage cooperation among edge cloud devices, content popularity, and GPS trajectory information to improve content delivery speeds, network traffic congestion, cache resource utilization efficiency, and users' quality of experience in highly populated cities. More specifically, our proposed approaches exploit collaborative filtering theory to provide accurate and efficient content popularity predictions to enable proactive in-network caching of Internet content. We propose a practical content delivery architecture that consists of standalone edge cloud devices to be deployed in the city to cache and process popular Internet content as it disseminates throughout the network. We also show that our proposed approaches ensure responsive cloud content delivery with minimized service disruption.