A Cloud-Edge Collaborative Gaming Framework Using AI-Powered Foveated Rendering and Super Resolution

IF 4.1 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Semantic Web and Information Systems Pub Date : 2023-04-20 DOI:10.4018/ijswis.321751

Xinkun Tang, Ying Xu, Ouyang Feng, Ligu Zhu, Bo Peng

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引用次数: 0

Abstract

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.

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使用ai驱动的注视点渲染和超分辨率的云边缘协作游戏框架

云游戏(CG)逐渐流行起来。通过在云端平衡共享计算资源，CG技术允许那些没有昂贵硬件的人使用低端设备享受AAA级游戏。然而，流媒体游戏视频对带宽的要求很高，大规模部署会导致骨干网拥塞，带宽费用昂贵。为了应对这一挑战，作者提出了一种创新的边缘辅助计算架构，该架构协同使用人工智能驱动的注视点渲染(FR)和超分辨率(SR)。使用FR，云服务器可以以较低的分辨率流式传输游戏视频，显著减少传输的数据量。然后，边缘服务器将使用特定于游戏的SR模型对视频进行升级，恢复视频的质量，特别是对于玩家最关注的区域。作者构建了一个名为FRSR的原型系统，并进行了全面、客观的对比实验，证明该架构与经典CG实现相比，在相同的感知质量下，可以减少39.47%的带宽使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal on Semantic Web and Information Systems 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.50%

发文量

审稿时长

20 months

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.