Abstract
Predictive Compute Allocation (PCA) is an approach for managing media transcoding by analyzing content at the moment of ingestion. The system performs a multimodal content analysis, evaluating both the visual complexity of a video and its associated text metadata to forecast processing requirements. Based on this analysis, the system generates a Multimodal Predictive Transcode Matrix. An orchestration compiler uses this matrix to dynamically generate a custom Directed Acyclic Graph (DAG) for the specific video asset. The DAG serves as an instruction set for the compute infrastructure, specifying which transcoding tasks to perform and how to route them to different hardware tiers. For content predicted to have low demand, the DAG can route the job to low-cost hardware for basic encoding while deferring high-resolution transcodes. This forecasting of resource needs reduces operational costs associated with brute-force transcoding and mitigates unpredictable compute spikes found in reactive systems.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Agarwal, Nikita, "Predictive Compute Allocation and Dynamic DAG Generation for Media Pipelines Using Multimodal Content Analysis", Technical Disclosure Commons, (July 16, 2026)
https://www.tdcommons.org/dpubs_series/10992