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Cloud gaming enables users to play high-quality games on low-end devices by offloading game processing to the cloud; however, network latency remains a significant issue affecting the gaming experience. Speculative execution is a promising approach to hide network latency by predicting and transmitting future frames early. However, existing methods generate excessive compute load and network traffic because of the numerous potential input patterns. This paper proposes a pattern reduction method using bit field representations of inputs to enable efficient speculative execution for cloud gaming. Two methods are introduced: temporal pattern analysis to detect frequent transitions and Long Short-Term Memory (LSTM) based prediction to estimate input probabilities. Experiments using actual gaming data show that the proposed methods significantly reduce rendered frames and network traffic versus prior speculative execution methods. The results demonstrate the method&#x2019;s effectiveness and scalability across diverse game genres.

Pattern Reduction for Low-Traffic Speculative Video Transmission in Cloud Gaming System