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With the growing demand for high-quality video streaming, the necessity of efficient methods to balance video quality and bandwidth consumption has become increasingly critical to ensure seamless user experiences and optimize network resources. Traditional adaptive streaming techniques often respond to network fluctuations, leading to delays and quality degradation. In this paper, we introduce a new AI-powered approach for adaptive HEVC transmission that is trained to predict bandwidth variations and adjust the encoding parameters to ensure optimal Quality of Service. The proposed approach uses models trained on historical and real-time network data to guarantee seamless transitions and reduce buffering to ensure a better user experience overall. The results of this work demonstrate that our proposed system outperforms traditional techniques in terms of video quality and stability during network fluctuations.
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DOI: 10.1109/iraset64571.2025.11007988
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