Streaming of rendered content with adaptive frame rate and resolution
For mobile users and service providers, this work improves perceived quality of streamed rendered content under bandwidth constraints with minimal infrastructure changes.
The paper addresses the problem of streaming rendered content to mobile devices under bandwidth constraints by adaptively adjusting both frame rate and resolution based on scene content and motion. The proposed system uses a lightweight neural network to predict optimal settings, significantly enhancing perceptual quality while minimizing computational cost.
Streaming rendered content is an attractive way to bring high-quality graphics to billions of mobile devices that do not have sufficient rendering power. Existing solutions render content on a server at a fixed frame rate, typically 30 or 60 frames per second, and reduce resolution when bandwidth is restricted. However, this strategy leads to suboptimal rendering quality under the bandwidth constraints. In this work, we exploit the spatio-temporal limits of the human visual system to improve perceived quality while reducing rendering costs by adaptively adjusting both frame rate and resolution based on scene content and motion. Our approach is codec-agnostic and requires only minimal modifications to existing rendering infrastructure. We propose a system in which a lightweight neural network predicts the optimal combination of frame rate and resolution for a given transmission bandwidth, content, and motion velocity. This prediction significantly enhances perceptual quality while minimizing computational cost under bandwidth constraints. The network is trained on a large dataset of rendered content labeled with a perceptual video quality metric. The dataset and further information can be found at the project web page: https://www.cl.cam.ac.uk/research/rainbow/projects/adaptive_streaming/.