Latency Reduction and Input Prediction for Cloud Gaming Clients
browser-based; clientside; cloud gaming; input prediction; latency; real-time systems
Cloud gaming enables access to high-quality games on thin clients by streaming rendered content from remote servers, but network-induced latency remains a critical barrier to responsive gameplay. This paper presents a browser-based system that profiles user input in real-time, employs a lightweight machine learning model to predict actions, and dynamically compensates for lag by speculative input. Our solution reduces perceived lag by up to 25% and maintains a 94%+ prediction accuracy, fully within a free-tier cloud environment. Compared to traditional infrastructure-based approaches, our method imposes no proprietary hardware requirements and offers platform-wide scalability. 2025 IEEE.
Payankan, Gavin George; Singal, Neha; Saxena, Surabhi
Proceedings - 2025 International Conference on Transformative Computing Technologies, ICTCT 2025;pp.30-34
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/ICTCT69201.2025.00023 https://www.scopus.com/pages/publications/105037433918?origin=resultslist
Payankan G.G., Christ University, Dept. of Computer Science, Bengaluru, India; Singal N., Christ University, Dept. of Computer Science, Bengaluru, India; Saxena S., Christ University, Dept. of Computer Science, Bengaluru, India