This paper introduces a privacy-aware video streaming framework that aims to balance Quality of Experience (QoE) and data confidentiality in edge computing environments. Leveraging adaptive bitrate streaming, lightweight encryption, and privacy-preserving data analytics, the proposed framework addresses the dual challenges of maintaining high QoE while ensuring robust privacy protections. The framework’s multi-objective optimization engine dynamically adjusts streaming parameters to maximize resolution and minimize latency while adhering to stringent privacy constraints. Performance evaluations using real-world datasets and simulations demonstrate significant improvements over baseline methods, achieving superior QoE metrics with reduced data leakage risk and manageable computational overhead. The results highlight the framework’s scalability and applicability to diverse scenarios, including live streaming, IoT surveillance, and telemedicine.