Large-Scale Real-World Evaluation of Adaptive QR-Based Edge-to-Cloud Video Ingestion Across 132 Heterogeneous Edge Deployments

Large-scale field video archiving under real-world operational conditions presents four compounding challenges: heterogeneous edge devices with inconsistent hardware capabilities, low-light and high-variance illumination environments, intermittent satellite and mobile network connectivity, and arbitrary camera mounting orientations. Existing QR-based video ingestion pipelines fail to address these challenges jointly, resulting in high decode failure rates and unreliable cloud archiving in distributed field deployments. This paper presents a framework that directly addresses all four challenges through a unified edge-to-cloud pipeline. Rather than introducing new computer-vision primitives, the contribution lies in the robust system architecture that integrates and orchestrates established techniques, and in its engineering validation at scale. The pipeline combines four engineered components: (i) a visibility-aware adaptive transcoding strategy; (ii) a priority-weighted non-uniform temporal sampling scheme; (iii) a 23-angle rotation ensemble decoder; and (iv) a checkpoint-resumable block-staged cloud synchronization mechanism with deferred reclassification. The framework is validated on 132 independent edge deployments across 849 operational regions, processing 87,873 production videos totaling 1.05 TB over a 10-day observation window (27 May–5 June 2026). The evaluation demonstrates 92.1% QR recall at 26.8% of full-scan CPU cost, 98.0% upload reliability, and 33.5% deferred recovery of initially unresolvable codes (p < 0.001, Cohen’s d > 0.97). To the best of our knowledge, this constitutes the largest reported real-world evaluation of an edge QR-based video ingestion framework and the first to characterize recall variance across more than 100 independent field deployments.