Shared Raytracing Context for Multi-Client Video Streaming
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Solution Overview
Problem
Conventional multi-client video streaming systems consume high server resources due to maintaining separate raytracing contexts for each client device, leading to inefficient memory and power usage.
Innovation Solution
Implementing a shared raytracing context at the server to generate video streams for multiple client devices, using a single graphics context including a bounding volume hierarchy (BVH) to perform raytracing operations, thereby reducing the need for multiple copies of the context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate raytracing contexts are maintained for each client device, then each client can receive customized video streams, but server memory consumption and power usage increase significantly
Solution Approach 1:
The patent merges multiple separate raytracing contexts into a single shared context that serves multiple client devices. The shared context includes common scene geometry, lighting, and material properties that are identical across different client views, eliminating redundant memory storage and computation while still allowing customization through view-specific parameters like camera position and frustum culling boundaries.
2Adaptability or versatility
If separate raytracing contexts are maintained for each client device, then each client can receive customized video streams, but server power consumption increases
Solution Approach 1:
The patent combines multiple raytracing operations into a single shared context execution, reducing the total computational power required. By maintaining one instance of the raytracing engine with shared scene data structures, the server avoids the overhead of initializing and maintaining multiple independent contexts, thereby reducing CPU/GPU power consumption while still delivering customized streams through selective rendering parameters.
3Quantity of substance
If a shared raytracing context is used for multiple client devices, then server memory and power resources are conserved, but the ability to handle diverse client requirements may be limited
Solution Approach 1:
The patent applies local quality by maintaining a single shared raytracing context for common scene elements while allowing client-specific customization through local parameters such as camera position, view frustum, and rendering resolution. This approach preserves memory efficiency while adapting the rendering output to meet diverse client requirements through selective modification of only the necessary view-dependent parameters.
4Ease of operation
If multiple copies of raytracing context are maintained, then context switching between clients is simpler, but resource efficiency deteriorates
Solution Approach 1:
The patent creates a universal shared raytracing context that serves multiple client devices simultaneously, eliminating the need for multiple separate context copies. The single context is designed to handle multiple client requests by accepting client-specific parameters (camera position, view angle, resolution) as inputs, thereby achieving both resource efficiency and operational flexibility without requiring context switching between multiple instances.
Data Source
AI summary
A server employs a shared raytracing context to generate video streams for multiple client devices. The server uses the shared raytracing context to perform raytracing operations for each of the client devices, and based on the raytracing operations generates different sets of image frames. The server then streams each set of image frames to a corresponding client device over a network.