Xinkun Tang, Ying Xu, Ouyang Feng, Ligu Zhu, Bo Peng
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A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution
Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.
期刊介绍:
The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.