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/.

翻译：渲染内容流式传输是一种将高质量图形传输至数十亿不具备足够渲染能力的移动设备的理想方式。现有解决方案以固定帧率（通常为每秒30或60帧）在服务器端渲染内容，并在带宽受限时降低分辨率。然而，该策略在带宽限制下会导致渲染质量欠佳。本研究利用人类视觉系统的时空极限，通过根据场景内容和运动自适应调整帧率与分辨率，在降低渲染成本的同时提升感知质量。该方法与编解码器无关，且仅需对现有渲染基础设施进行极简修改。我们提出一种系统，其中轻量级神经网络可针对给定传输带宽、内容及运动速度预测帧率与分辨率的最优组合。该预测能在带宽约束下显著增强感知质量，同时最小化计算成本。网络基于大规模标注有感知视频质量指标的渲染内容数据集进行训练。数据集及更多信息请访问项目网页：https://www.cl.cam.ac.uk/research/rainbow/projects/adaptive_streaming/。