Adaptive Virtualized Rendering Pipeline for Cloud-Client Distribution
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Solution Overview
Problem
Current real-time rendering technologies on mobile devices face challenges in achieving high image quality with low latency and high frame rates due to insufficient computing power, and existing cloud-client combined rendering solutions require pre-designed static selection methods that fail to adapt dynamically to changing optimization parameters.
Innovation Solution
A rendering framework based on an adaptive virtualized rendering pipeline that defines rendering resources, algorithms, and their read-write relationships, allowing for real-time selection and adjustment of cloud-client computing distribution solutions based on user-defined optimization objectives and budgets, enabling dynamic optimization and switching of rendering processes to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rendering operations are executed on cloud server with strong computing ability, then image quality and frame rate are improved, but network latency and transmission time increase
Solution Approach 1:
The rendering pipeline is segmented into multiple stages (geometry processing, rasterization, shading, etc.), and different stages are distributed between cloud server and client device. The cloud server executes computationally intensive stages while the client handles less demanding stages, achieving a balance between image quality and latency.
Solution Approach 2:
The patent introduces a new dimension of spatial distribution by separating the rendering pipeline across different locations (cloud server and client device). This spatial distribution allows the system to leverage the strong computing power of the cloud while maintaining lower latency through local client-side processing.
2Productivity
If more rendering pipeline stages are executed on cloud server, then frame rate is improved, but network bandwidth consumption increases
Solution Approach 1:
Different stages of the rendering pipeline are assigned to different locations based on their computational characteristics. Stages requiring high computation (like complex shading) are executed on the cloud server, while stages requiring frequent updates or low-latency response are executed locally on the client device, optimizing the trade-off between frame rate and bandwidth consumption.
3Device complexity
If static cloud-client computing distribution solution is used, then device complexity is reduced, but adaptability to changing optimization parameters deteriorates
Solution Approach 1:
The patent implements a dynamic selection mechanism that automatically adjusts the cloud-client computing distribution solution based on real-time optimization parameters such as network conditions, device performance, and user preferences. This dynamic adaptation resolves the contradiction by allowing the system to optimize for different parameters at different times without requiring complex manual configuration.
Solution Approach 2:
The system employs an automatic selection mechanism that autonomously determines the optimal rendering pipeline distribution without requiring manual intervention from developers or users. The mechanism evaluates multiple candidate solutions and selects the most appropriate one based on current optimization parameters, reducing device complexity while maintaining high adaptability.
Data Source
AI summary
The present invention discloses a cloud-client rendering computing method based on an adaptive virtualized rendering pipeline, comprising the following steps of: defining a rendering pipeline, including defining a rendering resource, a rendering algorithm, and a read-write relationship between the rendering algorithm and the rendering resource; selecting an optimal cloud-client computing distribution solution in a real-time manner from a cloud-client computing distribution solution set comprising each rendering resource that is allocated to a cloud or client for computing, based on self-defined optimization objectives and an optimization budget of a framework user; and executing a corresponding rendering algorithm on cloud and/or on a client according to the cloud-client computing distribution solution, thereby obtaining a rendering result. The rendering framework can adaptively select the cloud-client computing distribution solution upon cloud-client combined rendering and dynamically adjust it with a change of an optimization parameter.