In distributed multimedia applications, content is often delivered to users in a degraded form due to network-induced lossy compression. Real-time and interactive use cases like cloud gaming, which render content on the fly, require low latency and are hosted at resource-constrained edge servers. We present a new insight: when rendered content is delivered over a network with lossy compression, high-quality rendering can be ineffective in improving user-perceived quality, leading to a poor return on computing resources. Leveraging this observation, we built Stimpack, a novel system that adaptively optimizes game rendering quality by balancing server-side rendering costs against user-perceived quality. The system uses a mechanism that quantifies the efficiency of resource usage to maximize overall system utility in multi-user scenarios. Our open-sourced implementation and extensive evaluations show that Stimpack achieves up to 24% higher service quality and serves twice as many users with the same resources compared to baselines. A user study further validates that Stimpack provides a measurably better user experience.

翻译：在分布式多媒体应用中，内容常因网络有损压缩而以降质形式传输给用户。云游戏等实时交互场景需即时渲染内容，对低延迟有严格要求，且通常托管于资源受限的边缘服务器。我们提出一项新洞察：当渲染内容经有损压缩网络传输时，高质量渲染可能无法有效改善用户感知质量，导致计算资源投入回报低下。基于此发现，我们构建了Stimpack——一种通过平衡服务器端渲染成本与用户感知质量来自适应优化游戏渲染质量的新型系统。该系统采用量化资源使用效率的机制，在多用户场景下最大化系统整体效用。我们的开源实现与大量评估表明，相比基线方案，Stimpack在同等资源下可将服务质量提升高达24%，并服务两倍数量的用户。用户研究进一步验证了Stimpack能带来可测量的更好用户体验。