Enhancing Cloud Gaming QoE Estimation by Stacking Learning

doi:10.1007/s10922-024-09836-6

2024-06-12

Daniel Soares *

Marcos Carvalho

Daniel F. Macedo

DOI：10.1007/s10922-024-09836-6

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摘要

En 中文

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.

Keyword:

Cloud gaming

QoE

Machine learning

期刊

Journal of Network and Systems Management

IF:

3.9

论文数:

1.0K

被引数:

1.3K

机构

Universidade Federal de Minas Gerais

学者数:

2.5W

论文数: 1.5W

被引数: 1.4W

引用论文

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