Dynamic CPU Cycle Allocation for Video Stream Processing
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Solution Overview
Problem
Existing digital video processing systems inefficiently allocate CPU cycles, leading to suboptimal performance due to fixed resource allocation, which does not account for varying complexity of digital video streams over time.
Innovation Solution
Implementing a dynamic CPU cycle allocation system that periodically assesses the complexity of digital video streams and adjusts CPU cycles for each encoding module to ensure optimal resource utilization, reallocating cycles as needed to maintain desired video quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed resource allocation is used for CPU cycles in video stream processing, then resource allocation simplicity is maintained, but processing efficiency deteriorates due to inability to adapt to varying video complexity
Solution Approach 1:
The patent implements dynamic CPU cycle allocation where the system continuously monitors video stream complexity metrics (such as motion intensity, scene changes, and encoding difficulty) and adjusts CPU cycle distribution in real-time. This transforms the static resource allocation into a dynamic system that adapts to changing processing requirements, thereby improving processing efficiency without requiring fundamentally complex allocation mechanisms
Solution Approach 2:
The system employs feedback mechanisms by monitoring video complexity metrics and using this information to adjust CPU cycle allocation. The complexity assessment feedback loop enables the system to automatically redistribute computational resources based on actual processing needs, resolving the contradiction between allocation simplicity and processing efficiency
2Manufacturing precision
If CPU cycles are reallocated dynamically based on video complexity, then video quality is improved, but system complexity increases
Solution Approach 1:
The patent segments the video processing system into distinct modules: video complexity analysis module, CPU cycle allocation module, and video encoding module. Each module performs a specific function, and the complexity of quality assessment is isolated from the encoding process. This segmentation allows high video quality to be achieved through coordinated module operation without requiring the entire system to be fundamentally complex
Solution Approach 2:
The system performs preliminary complexity assessment on video streams before encoding begins. By pre-evaluating video complexity metrics and pre-allocating CPU cycles accordingly, the system ensures optimal video quality from the start of encoding without requiring complex real-time adjustments during the encoding process itself
3Productivity
If equal fixed share of transponder capacity is assigned to each service, then resource allocation simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent changes the allocation parameter from fixed equal shares to dynamic shares based on video complexity parameters. The system adjusts the proportion of CPU cycles allocated to each video stream according to measured complexity metrics, enabling high resource utilization efficiency. The parameter change transforms allocation from a simple equal-distribution rule to a metric-driven dynamic distribution system
Data Source
AI summary
Approaches for dynamically allocating compute capacity for processing a video stream. Video complexity information for two or more digital video streams actively being processed by one or more video encoders is determined at periodic intervals. Video complexity information describes the complexity of digital video carried by the digital video streams across a bounded number of consecutive digital frames which includes digital frames not yet processed by the one or more video encoders. A determination is made as to whether the compute capacity allocated for processing a particular digital video stream should be adjusted in some manner based on the determined video complexity information. The amount of compute capacity allocated for processing the particular digital video stream may be dynamically adjusted in response to maximizing a measure of optimal video quality calculated for the two or more digital video streams using, at least in part, the determined video complexity information.