  The rapid production of mobile devices along with the wireless applications
boom is continuing to evolve daily. This motivates the exploitation of wireless
spectrum using multiple Radio Access Technologies (multi-RAT) and developing
innovative network selection techniques to cope with such intensive demand
while improving Quality of Service (QoS). Thus, we propose a distributed
framework for dynamic network selection at the edge level, and resource
allocation at the Radio Access Network (RAN) level, while taking into
consideration diverse applications' characteristics. In particular, our
framework employs a deep Multi-Agent Reinforcement Learning (DMARL) algorithm,
that aims to maximize the edge nodes' quality of experience while extending the
battery lifetime of the nodes and leveraging adaptive compression schemes.
Indeed, our framework enables data transfer from the network's edge nodes, with
multi-RAT capabilities, to the cloud in a cost and energy-efficient manner,
while maintaining QoS requirements of different supported applications. Our
results depict that our solution outperforms state-of-the-art techniques of
network selection in terms of energy consumption, latency, and cost.
