Abstract
Real-time video streaming over wireless networks has become increasingly reliant on adaptive bitrate (ABR) control to mitigate variability in bandwidth, latency, and user mobility. However, existing ABR algorithms are predominantly reactive, operate on limited network observability, and largely ignore the computational and bandwidth overhead introduced by encryption, which is now ubiquitous in edge-assisted multimedia delivery. This paper presents a machine-learning driven adaptive bitrate optimization framework that jointly addresses predictive bandwidth estimation, mobility dynamics, and security constraints in edge-assisted video transmission. We formulate bitrate selection as a stochastic optimization problem and develop a cross-layer system model that characterizes network evolution, user mobility, and cryptographic overhead. An edge-hosted learning engine leverages supervised prediction and reinforcement-driven control to proactively select bitrates using features derived from transport behavior, playback state, and security cost. We implement the proposed approach in a prototype edge-streaming platform and evaluate performance under realistic wireless traces, user mobility patterns, and multi-user contention. Experimental results demonstrate that the proposed system reduces stall probability by up to 42%, improves average Quality of Experience (QoE) by up to 27%, and maintains equitable performance under multi-user load, while introducing only modest cryptographic overhead. We further analyze the security–performance trade-offs, identify risk factors in encrypted media pipelines, and quantify the operational limits of edge execution. The results highlight the importance of integrating prediction, security-awareness, and scalability into ABR design, and demonstrate the potential of edge-hosted learning models to enable secure, high-quality, and resource-efficient video streaming in mobile environments.
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