Ensuring consistent video Quality of Experience (QoE) across modern distribution pipelines has become increasingly challenging as content passes through multiple transformations—from acquisition to encoding, transcoding, packaging, CDN delivery, and final device playback. This proposal presents a unified framework for measuring and monitoring video quality at each stage of the end-to-end workflow using a hybrid combination of reference, reduced-reference, and non-reference methods.

The approach integrates industry metrics such as VMAF, VMAF NEG, and UVQ for perceptual scoring, along with other open source AI/ML-based non-reference models that detect degradation without requiring a source feed. The system identifies compression artifacts, frame freezes, macroblocking, texture loss, audio issues, and representation inconsistencies across ABR ladders and devices. By correlating these objective measures with predicted QoE, the framework isolates the exact stage where degradation occurs—whether during ingest, encoder/transcoder processing, CDN propagation, or player rendering.

The proposal highlights how combining traditional metrics with AI-driven quality estimation creates a more reliable and scalable methodology for Live, Linear, VOD, and FAST workflows. It introduces the concept of a “Quality Fingerprint,” enabling cross-stage attribution and trend analysis across the delivery chain. The goal is to offer broadcasters, streamers, and service providers a practical, unified solution to measure, compare, and optimize video quality from acquisition to device playback, improving overall viewing experience and operational efficiency.