GPU Stream Processing for Low-Latency Video Packetization
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Solution Overview
Problem
Conventional systems face challenges in processing multiple encoded video streams using a CPU, leading to increased CPU resource utilization and latency, especially when generating and encoding multiple video streams simultaneously, which can affect frame rate and processing efficiency.
Innovation Solution
Offload CPU-intensive tasks such as packetization, forward error correction (FEC), and encryption to graphics processing units (GPUs) to reduce latency and CPU resource usage, allowing GPUs to perform these processes in parallel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a CPU processes multiple encoded video streams using a processing stack (packetization, FEC, encryption), then the video streams can be sent to client devices, but CPU resource utilization increases and processing latency increases
Solution Approach 1:
The patent replaces the CPU-based processing stack (packetization, FEC, encryption) with a GPU-based processing system. The GPU's parallel processing architecture substitutes the traditional sequential CPU operations, enabling simultaneous processing of multiple video streams without increasing latency. This mechanical substitution resolves the contradiction by maintaining high throughput while eliminating the time loss associated with CPU processing.
Solution Approach 2:
The patent segments the video processing pipeline by separating encoding (GPU) from processing (GPU). The processing stack operations are divided into independent parallel tasks that can be executed simultaneously on different GPU cores, allowing multiple video streams to be processed concurrently without competing for CPU resources. This segmentation enables high productivity without latency penalties.
2Reliability
If a CPU dedicates a given number of cores to a video stream, then the video stream can be processed, but the CPU cannot process the video stream without increasing processing latency or reducing frame rate
Solution Approach 1:
The patent transitions from single-threaded CPU processing to multi-threaded GPU processing, adding a dimension of parallelism. Instead of dedicating a limited number of CPU cores sequentially, the GPU provides hundreds of cores that can process video stream data in parallel across multiple threads and warps. This dimensional change in processing architecture maintains reliable video stream processing while dramatically improving frame rate throughput.
3Adaptability or versatility
If a GPU encodes multiple video streams associated with different application sessions, then more content can be delivered, but the CPU requires large amounts of resources and increased latency to process the encoded streams
Solution Approach 1:
The patent implements a universal GPU-based processing stack that can handle multiple video streams from different application sessions simultaneously. The same GPU hardware and processing algorithms serve multiple functions and multiple clients, eliminating the need for separate CPU processing paths for each stream. This multi-functionality approach enables adaptive multi-stream encoding while minimizing CPU resource consumption.
Data Source
AI summary
In various examples, processing content data using graphics processing units for video streaming systems and applications is described herein. Systems and methods are disclosed that offload at least a portion of the processing that is typically performed by a central processing unit (CPU) to a graphics processing unit (GPU). For example, and for a streaming application, the GPU may initially generate and then encode a content stream (e.g., a video stream, an audio stream, etc.). In some examples, the GPU generates and/or encodes the content stream based on input data received from one or more client devices. The GPU may then perform additional processing associated with the encoded content stream, such as packetization, forward error correction (FEC), encryption, and/or any other processing. The CPU may then use packet pacing when causing the processed content stream to be streamed to the client device(s).