Intelligent Video Frame Grouping for Parallel Hardware Utilization
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Solution Overview
Problem
Current methods for video frame encoding in on-demand video services lead to underutilization of parallel hardware due to inefficient frame bundling, resulting in suboptimal performance and resource utilization during high workloads.
Innovation Solution
Implementing a system that uses weighted heuristics to predict and group frames based on predicted performance, allowing for intelligent frame bundling that maximizes hardware resource utilization by clustering frames that finish at similar times, thereby optimizing the use of multiple video motion estimation engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frames are bundled using traditional methods, then processing throughput is maintained, but hardware resource utilization deteriorates due to underutilization of parallel motion estimation engines
Solution Approach 1:
The system performs preliminary analysis of frame characteristics (motion complexity, resolution, encoding difficulty) before bundling frames together. This preliminary action allows the system to predict processing time and group frames that will complete at similar times, ensuring full utilization of parallel motion estimation engines throughout the processing pipeline without idle periods
Solution Approach 2:
The frame bundling system dynamically adjusts grouping strategies based on real-time workload characteristics and hardware availability. Instead of static bundling rules, the system adapts frame groupings to match the actual processing capacity and performance of motion estimation engines, optimizing resource utilization as conditions change
2Power
If more parallel hardware is deployed, then processing capacity increases, but resource utilization deteriorates due to idle hardware during low-workload periods
Solution Approach 1:
The system merges multiple video streams and frames into consolidated processing batches that fully utilize available parallel hardware. By combining workloads from multiple sources and intelligently grouping frames with compatible processing requirements, the system ensures that all motion estimation engines remain actively utilized even when individual video streams have variable workloads
3Ease of operation
If frames are processed individually, then processing flexibility is maintained, but productivity deteriorates due to inability to fully utilize parallel processing capabilities
Solution Approach 1:
The system segments the video processing workload into discrete, independently processable frame units while maintaining logical groupings. Each frame can be processed independently by separate motion estimation engines, preserving flexibility, while the segmentation enables efficient distribution of work across parallel hardware resources without inter-frame dependencies
Data Source
AI summary
Systems, apparatuses and methods may include technology to bundle on demand video frames together in clusters having similar encode times based on predicted performance determined by weighted heuristics.