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Enhancing Cloud Gaming QoE Estimation by Stacking Learning

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Abstract

The Cloud Gaming sector is burgeoning with an estimated annual growth of more than 50%, poised to reach a market value of $22 billion by 2030, and notably, GeForce Now, launched in 2020, reached 20 million users by August 2022. Cloud gaming presents cost-effective advantages for users and developers by eliminating hardware investments and game purchases, reducing development costs, and optimizing distribution efforts. However, it introduces challenges for network operators and providers, demanding low latency and substantial computational power. User satisfaction in cloud gaming depends on various factors, including game content, network type, and context, all shaping Quality of Experience. This study extends prior research, merging datasets from wired and mobile cloud gaming services to create an Expanded stacking model. All data gathering involves actual users engaging in gameplay within a realistic test environment, employing protocols akin to those utilized by the Geforce Now cloud gaming platform. Results indicate significant improvements in QoE estimation across different gaming contexts, highlighting the feasibility of a versatile predictive model for cloud gaming experiences, building upon previous stacking learning approaches.

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Data Availability

No datasets were generated or analysed during the current study.

Notes

  1. We started the collection with GeForce Experience and Gamestream, but Nvidia ended support by February of 2023 [22]. We then switched to sunshine, an open source version of Gamestream that start development in 2020 [23]. We did several tests comparing the Gamestream and sunshine, and there is no lost/gain in performance.

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Acknowledgements

This work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brasil (CAPES)-Finance Code 001, CNPq (funding agency from the Brazilian federal government), FAPEMIG (Minas Gerais State Funding Agency), and São Paulo Research Foundation (FAPESP) with Brazilian Internet Steering Committee (CGI.br), grants 2018/23097-3 and 2020/05182-3.

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Author notes

  1. Daniel Soares, Marcos Carvalho and Daniel F. Macedo have contributed equally to this work.

Authors and Affiliations

  1. DCC, Universidade Federal de Minas Gerais, Street Reitor Píres Albuquerque, Belo Horizonte, MG, 31270-901, Brazil

    Daniel Soares, Marcos Carvalho & Daniel F. Macedo

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  1. Daniel Soares
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  2. Marcos Carvalho
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Each of the three authors made equal contributions to this work. We engaged in collective discussions throughout the developmental phase of the article, collaborated on the writing process, and collectively reviewed each other's sections.

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Correspondence to Daniel Soares.

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Soares, D., Carvalho, M. & Macedo, D.F. Enhancing Cloud Gaming QoE Estimation by Stacking Learning. J Netw Syst Manage 32, 58 (2024). https://doi.org/10.1007/s10922-024-09836-6

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