Optimization of Video Content Delivery in Hybrid Cloud Architectures

The article presents an analysis of architectural, network, and algorithmic factors that determine the quality of experience in hybrid cloud-edge video delivery systems, with a focus on their impact on delivery stability and QoE parameters.The study is conducted within an interdisciplinary paradigm combining distributed computing theory, network engineering, multimedia processing, and resource management in wireless networks.The methodological basis is a content analysis of peer-reviewed publications on low-latency bitrate adaptation, software-defined intercloud backbones with points of presence, GPU-accelerated encoding, and SDN control using the -Lyapunov drift-plus-penalty function.Three interrelated groups of optimization mechanisms are identified and systematized: transport and routing (PoP-Overlay, jitter reduction, latency predictability), computational (multi-level caching, serverless GPU processing), and algorithmic (CMAF with chunked transfer, low-latency ABR strategies, content-aware scheduling).Three analytical tables are presented, including an intercontinental backbone comparison, HEVC accelerated encoding metrics, and comparative results of adaptive streaming algorithms.It is shown that the coordinated integration of transport, computational, and algorithmic solutions forms a predictable delivery loop that minimizes playback stalls and abrupt quality switches.The findings substantiate the need to design cloud-edge video systems based on QoE-oriented management models.The article will be of interest to researchers in multimedia networking, cloud and edge platform engineers, adaptive video streaming developers, and specialists in distributed architecture optimization.