Enhancing personalized video streaming through contextual information and support for emerging video formats

Abstract This study presents an intelligent personalized video streaming framework that integrates contextual awareness and support for emerging video formats such as HDR and $$360^{\circ }$$ videos. Using a deep learning-based QoE prediction model, the proposed system adapts bitrate and resolution dynamically based on network conditions, device capabilities, and user preferences. Experimental results across diverse video formats show that our approach achieves up to 17.4% improvement in QoE compared to baseline models (NARX, Decision Tree, and Linear Regression), with statistically significant differences (p < 0.05). The framework supports deployment across edge and cloud infrastructure with consideration for real-time constraints. These results demonstrate the feasibility of our system for scalable, real-world adaptive streaming.