Runtime-Configurable Multi-Threaded Video Pipeline for Low Latency
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Solution Overview
Problem
Existing video processing systems face inefficiencies in network latency, bandwidth utilization, and dynamic device integration due to predefined video processing pipelines, leading to unnecessary data transmission and inefficient processing.
Innovation Solution
A multi-threaded video pipeline is configured at runtime to optimize video processing threads based on real-time conditions, allowing asynchronous execution and dynamic device integration, minimizing latency and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If predefined video processing pipelines are used, then system configuration is simplified, but network latency increases and bandwidth utilization becomes inefficient
Solution Approach 1:
The video processing pipeline transitions from a static predefined configuration to a dynamic runtime configuration that adapts to real-time conditions. The system configures video processing threads, encoding parameters, and device integration based on current network status, device capabilities, and traffic conditions, thereby reducing latency and optimizing bandwidth utilization without requiring complex manual configuration.
Solution Approach 2:
The system dynamically adjusts video processing parameters such as encoding bitrate, resolution, frame rate, and thread allocation based on real-time network conditions and device capabilities. This parameter adaptation allows the pipeline to optimize performance for current conditions, reducing network latency and improving bandwidth efficiency while maintaining simplified operation through automated parameter tuning.
2Ease of manufacture
If predefined video processing pipelines are used, then implementation is easier, but bandwidth utilization becomes inefficient due to unnecessary data transmission
Solution Approach 1:
The system dynamically adjusts video encoding parameters including bitrate, resolution, and compression settings based on real-time network bandwidth conditions. This ensures that only necessary data is transmitted at optimal quality levels, preventing bandwidth waste from unnecessary or over-provisioned data transmission while maintaining ease of implementation through automated parameter optimization.
Solution Approach 2:
The system extracts and transmits only the essential video data required for current processing needs, removing unnecessary data elements from transmission. By dynamically determining minimum required bandwidth based on real-time conditions, the system eliminates wasteful data transmission while maintaining simple implementation through automated data filtering and optimization.
3Ease of operation
If predefined video processing pipelines are used, then system setup is simpler, but dynamic device integration becomes difficult
Solution Approach 1:
The video processing pipeline becomes dynamically configurable at runtime to accommodate new video capture devices, processors, and network conditions. When devices are added or removed, the system automatically reconfigures thread allocation, encoding parameters, and data flow routes without requiring complex manual reconfiguration, thereby enabling easy dynamic device integration while maintaining simple operation through automated adaptation.
Solution Approach 2:
The system designs the video processing pipeline with universal interfaces and standardized protocols that can accommodate multiple types of video capture devices and processing units. This multi-functional architecture allows diverse devices to be integrated dynamically without requiring device-specific configuration complexity, maintaining ease of operation while enhancing adaptability to different device types and scenarios.
4Productivity
If more video processing threads are added, then processing efficiency improves, but system complexity increases
Solution Approach 1:
The video processing system implements self-service thread management where the pipeline automatically configures, allocates, and manages video processing threads based on real-time workload conditions and device capabilities. The system autonomously determines optimal thread counts, assigns tasks to appropriate threads, and adjusts resource allocation without requiring complex external configuration or management, thereby improving processing efficiency while keeping the system self-managing rather than externally complex.
Data Source
AI summary
Techniques are disclosed herein for providing a multi-threaded video pipeline for video content associated with a video environment. Examples may include detecting a defined event type with respect to raw video data associated with at least one video capture device located within a video environment, configuring respective video processors of a video processor pipeline associated with the at least one video capture device based at least in part on the defined event type, encoding video data associated with the at least one video capture device based at least in part on the configuration of the respective video processors, and outputting the encoded video data to a network device.