  Extended Reality (XR) services will revolutionize applications over 5th and
6th generation wireless networks by providing seamless virtual and augmented
reality experiences. These applications impose significant challenges on
network infrastructure, which can be addressed by machine learning algorithms
due to their adaptability. This paper presents a Multi- Agent Reinforcement
Learning (MARL) solution for optimizing codec parameters of XR traffic,
comparing it to the Adjust Packet Size (APS) algorithm. Our cooperative
multi-agent system uses an Optimistic Mixture of Q-Values (oQMIX) approach for
handling Cloud Gaming (CG), Augmented Reality (AR), and Virtual Reality (VR)
traffic. Enhancements include an attention mechanism and slate-Markov Decision
Process (MDP) for improved action selection. Simulations show our solution
outperforms APS with average gains of 30.1%, 15.6%, 16.5% 50.3% in XR index,
jitter, delay, and Packet Loss Ratio (PLR), respectively. APS tends to increase
throughput but also packet losses, whereas oQMIX reduces PLR, delay, and jitter
while maintaining goodput.
