Neural Network Frame Prediction for Cloud Gaming Latency
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Solution Overview
Problem
Cloud gaming applications face significant latency issues due to transmission delays, encoder latency, and decoder latency, particularly in network-based gaming, which can lead to user discomfort and increased latency variance.
Innovation Solution
A method utilizing a neural network to predict subsequent frames based on previous frames and user actions, reducing latency by generating predicted frames and replacing them with real frames in the frame buffer, thereby minimizing processing and transmission delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If cloud gaming architecture is used to enable remote rendering, then device cost and power consumption are reduced, but transmission latency and encoder/decoder latency increase
Solution Approach 1:
The system performs preliminary actions by predicting future game frames before they are actually rendered by the server. The client-side predictor generates predicted frames based on previous frames and user inputs, proactively creating content that will be needed soon, thereby reducing the effective latency when frames need to be displayed.
Solution Approach 2:
The system dynamically adapts the frame prediction process based on network conditions and game state. The predictor adjusts its operation to balance between using predicted frames (when network latency is high) and waiting for real frames (when network conditions are good), making the system flexible and adaptive to changing conditions.
2Measurement precision
If real-time frame transmission is used to ensure accurate game state display, then visual accuracy is maintained, but latency increases and user comfort deteriorates
Solution Approach 1:
The system creates copies of the rendering process by implementing a predictor at the client side that replicates the server's frame generation logic. This predictor generates approximate copies of future frames locally, reducing the need to wait for and transmit every single real frame, thereby reducing latency while maintaining acceptable visual accuracy.
Solution Approach 2:
The system changes the parameter of frame timing by displaying predicted frames at their predicted time rather than waiting for real frames. This temporal parameter change allows the system to present frames sooner, reducing perceived latency, while using fallback mechanisms to correct accuracy when real frames become available.
3Loss of time
If frame prediction is used to reduce latency, then response time is improved, but visual accuracy may deteriorate if predicted frames replace real frames
Solution Approach 1:
The system implements feedback mechanisms where the client compares predicted frames with incoming real frames and uses this information to improve future predictions. The feedback loop allows the system to learn from discrepancies between predicted and actual frames, progressively improving prediction accuracy while maintaining the latency benefits of prediction.
Solution Approach 2:
The system dynamically adjusts the balance between using predicted frames and real frames based on prediction accuracy and network conditions. When predictions are accurate and network latency is high, predicted frames are used more frequently. When predictions diverge from reality or network conditions improve, the system transitions to using real frames, making the approach adaptive rather than static.
Data Source
AI summary
A method for reducing a latency in a gaming application comprising: obtaining (305B) a first frame, said first frame being representative of a first action performed by a user in the gaming application; obtaining (500) information representative of a second action performed by the user in the gaming application, said second action following the first action; and, predicting (500) a second frame corresponding to the second action from data comprising at least the first frame and the information representative of a second action using a neural network.