Adrenaline: Adaptive Rendering Optimization System for Scalable Cloud Gaming

2024-12-27

AI Summary

Cloud gaming in edge-based multi-user scenarios suffers from degraded end-to-end visual quality and imbalanced resource allocation due to fixed computational capacity and volatile network conditions, leading to high compression distortion in rendered frames. To address this, we propose a perception-aware adaptive rendering co-optimization framework. First, we introduce a novel “rendering effectiveness score” that jointly models client-side visual degradation and server-side computational overhead. Second, we design a dynamic rendering control policy driven by real-time network feedback, enabling lightweight integration with game engines. Third, we establish a joint quantification model linking end-to-end visual quality to computational cost. Experimental results demonstrate up to a 24% improvement in service quality and a 100% increase in concurrent user capacity under identical resource constraints, compared to baseline approaches. The implementation is open-sourced and compatible with mainstream engines including Unity and Unreal Engine.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Computer Vision: Learning & Optimization for CV
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Cloud gaming requires a low-latency network connection, making it a prime candidate for being hosted at the network edge. However, an edge server is provisioned with a fixed compute capacity, causing an issue for multi-user service and resulting in users having to wait before they can play when the server is occupied. In this work, we present a new insight that when a user's network condition results in use of lossy compression, the end-to-end visual quality more degrades for frames of high rendering quality, wasting the server's computing resources. We leverage this observation to build Adrenaline, a new system which adaptively optimizes the game rendering qualities by considering the user-side visual quality and server-side rendering cost. The rendering quality optimization of Adrenaline is done via a scoring mechanism quantifying the effectiveness of server resource usage on the user-side gaming quality. Our open-sourced implementation of Adrenaline demonstrates easy integration with modern game engines. In our evaluations, Adrenaline achieves up to 24% higher service quality and 2x more users served with the same resource footprint compared to other baselines.

Problem

• Cloud Gaming
• Resource Allocation
• Quality of Experience

Innovation

• Adrenaline
• Smart Optimization
• Cloud Gaming