Personalized Cloud Gaming: Multi-Objective Optimization for Resource Utilization and Video Encoding

Cloud gaming represents a major part of contemporary gaming. To boost the Quality-of-Experience (QoE) of cloud gaming, the integration of Dynamic Adaptive Video Encoding (DAVE) with Multi-access Edge Computing (MEC) has become the natural candidate owing to its flexibility and reliable transmission...

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Veröffentlicht in:IEEE transactions on cloud computing Jg. 13; H. 3; S. 854 - 866
Hauptverfasser: Zhang, Jingjing, Deng, Xiaoheng, Gui, Jinsong, Chen, Xuechen, Wan, Shaohua, Min, Geyong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Bit rate
Cloud computing
Cloud gaming
Coding
Configurations
Customization
Decoupling
deep reinforcement learning
Edge computing
Encoding
Environment models
Game theory
Heuristic algorithms
Mobile computing
Multiagent systems
Multiple objective analysis
Network latency
Optimization
Quality of experience
quality-of-experience (QoE)
Real time
Rendering (computer graphics)
Resource allocation
Resource management
Resource utilization
Servers
Software-defined networking
Streaming media
video encoding configuration
ISSN:2168-7161, 2372-0018
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Zusammenfassung:Cloud gaming represents a major part of contemporary gaming. To boost the Quality-of-Experience (QoE) of cloud gaming, the integration of Dynamic Adaptive Video Encoding (DAVE) with Multi-access Edge Computing (MEC) has become the natural candidate owing to its flexibility and reliable transmission support for real-time interactions. However, as multiple gamers compete for limited resources to achieve personalized QoE, such as ultra-high video quality and ultra-low latency, how to support efficient edge resource optimization is a fundamental and important problem. Furthermore, determining the optimal game video encoding configuration in real-time poses significant challenges, especially when lacking the information on future video and edge network resources. To address these key issues, we jointly optimize the video encoding as well as computing and communication resource allocation by active mutual adaptation of video coding configurations and physical resources in a Software Defined Networking (SDN)-assisted edge network. This eliminates the performance bottleneck caused by decoupling optimization of coding parameter configuration and physical resource allocation. The SDN-assisted edge network architecture supports efficient on-demand resource management, provides global network information, and meets the stringent time-varying game requests. Due to the significant time scale difference between video chunk and physical resource block, we propose a novel Asynchronous Decision-Making Multi Agent Proximal Policy Optimization algorithm (AD-MAPPO), which can address the credit assignment problem with a single agent. It can also adapt to the highly dynamic cloud gaming environment without prior knowledge and a deterministic environmental model. Extensive experimentation based on real cloud gaming datasets convincingly demonstrates that our approach can significantly enhance the overall QoE of gamers.
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ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2025.3571095