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

Cloud gaming allows 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 due to many potential input patterns. This paper introduces a pattern reduction method that uses a bit field representation of the input and facilitates efficient speculative execution in cloud games. There are two pattern reduction techniques: analyzing temporal patterns to detect frequent transitions and using LSTM-based predictions 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's effectiveness and scalability across diverse game genres.