The importance of multimedia streaming using mobile devices has increased considerably. The dynamic adaptive streaming over\nHTTP is an efficient scheme for bitrate adaptation inwhich video is segmented and stored in different quality levels.Themultimedia\nstreaming with limited bandwidth and varying network environment for mobile users affects the user quality of experience. We\nhave proposed an adaptive rate control using enhancedDoubleDeep Q-Learning approach to improvemultimedia content delivery\nby switching quality level according to the network, device, and environment conditions. The proposed algorithm is thoroughly\nevaluated against state-of-the-art heuristic and learning-based algorithms.The performance metrics such as PSNR, SSIM, quality\nof experience, rebuffering frequency, and quality variations are evaluated. The results are obtained using real network traces which\nshows that the proposed algorithm outperforms the other schemes in all considered quality metrics. The proposed algorithm\nprovides faster convergence to the optimal solution as compared to other algorithms considered in our work.
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