Optimal tile-based bitrate allocation improves the Quality of Experience (QoE) for adaptive 360° video streaming across multiple clients in heterogeneous network environments; however, it is challenging as it implies accurate viewport prediction, finest tile-based bitrate reservation, and maintaining QoE fairness, particularly under constrained network conditions. This paper proposes a strategy named EDGE360, that employs an edge-driven Multi-Agent Deep Reinforcement Learning (MADRL) solution for rate adaptation to improve the joint QoE in DASH-based rich media content delivery based on adaptive viewport prediction and Video Multi-method Assessment Fusion (VMAF) corresponding tiling granularity selection. Cooperative strategies among agents in the central critic network are crucial for addressing the complexity of network instances at the edge and optimizing media streaming bitrate assignment in multipleclient scenarios. Therefore, EDGE360 aims to implement the Counterfactual Multi-Agent Policy Gradients (COMA) based on 5G network traces to train agents in policies that optimize individual client QoE and fairness among clients, resulting in an improved rich streaming experience. At the edge, a tilebased quality monitor evaluates viewport trajectories, buffer status, and network throughput, employing deep learning to forecast optimal tile bitrate allocation, which is formulated as an MDP and solved with MADRL. Based on extensive experimentation, EDGE360 surpasses state-of-the-art adaptive bitrate algorithms by achieving the highest average reward, outperforming RAPT360, 360SRL, and BOLA360 by 8.12%, 11.86%, and 18.00%, respectively, demonstrating superior convergence and refinement