Abstract

Multimedia transmission systems have faced significant performance degradation due to dynamic network conditions and heterogeneous user demands. The background of adaptive multimedia optimization has remained critical in supporting real time quality of service requirements across modern communication systems. The problem has been observed in inefficient allocation of bandwidth and inability of conventional methods to adapt to spatiotemporal variations. To address this issue a Spatiotemporal Deep Q Network based Reinforcement Learning framework has been proposed named STDRL for adaptive multimedia quality optimization. The framework has integrated convolutional feature extraction with temporal dependency modeling using recurrent structures that capture evolving network states. The model has been trained using reward driven policy optimization that balances latency throughput and perceptual quality metrics. Experimental evaluation demonstrates that the proposed method achieves 40.7 dB PSNR compared to 34.0 dB in baseline DQN Adaptive Streaming. SSIM improves to 0.98 compared to 0.91 in conventional methods. Latency reduces to 60 ms compared to 100 ms in baseline approaches, indicating faster adaptive response. Throughput increases to 16.5 Mbps compared to 13.2 Mbps, showing improved bandwidth utilization efficiency. QoE stabilizes at 5.0, indicating optimal user satisfaction. The reinforcement learning agent learns adaptive policies through reward driven optimization that balances visual quality, latency, and smoothness.

Authors

Mariam Safar Mohammed Alshahrani¹, M.K. Jayanthi Kannan², Shree Nee Thirumalai Ramesh³
Digital Government Authority of KSA, Riyadh Province, Kingdom of Saudi Arabia¹, VIT Bhopal University, India², Manipal University College Malaysia, Malaysia³

Keywords

Spatiotemporal Learning, Reinforcement Learning, Multimedia Optimization, Deep Q Network, Edge Computing