In-Session Calibration for Streaming App Lag and Frame Drop Control
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Solution Overview
Problem
Conventional systems fail to dynamically adjust settings during a gaming session to optimize performance due to changes in client device resources or power source, leading to increased lag and frame drops.
Innovation Solution
Systems monitor client device state and application performance during a session, using decision trees to dynamically update settings such as terminating applications, adjusting processor clocks, and optimizing graphics settings based on resource utilization and frame rates to maintain optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems analyze client device before a session to determine settings, then initial performance is optimized, but performance degrades when device state changes during the session
Solution Approach 1:
The system dynamically adjusts gaming application settings during the session based on real-time monitoring of device state changes. The calibration is no longer static but adapts continuously as resources are allocated or deallocated, power source changes, or other applications are launched, maintaining optimal performance throughout the session.
Solution Approach 2:
The system monitors device state information and performance metrics during the session, using this feedback to trigger recalibration events. When state changes are detected (such as GPU utilization thresholds or frame drop rates), the system responds by updating settings, creating a closed-loop control system that maintains performance consistency.
2Reliability
If settings are updated dynamically during session, then performance consistency is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of device state during the session to predict when performance degradation may occur. By monitoring state information proactively and using decision trees to anticipate needed adjustments, the system can prepare and apply settings updates before significant performance degradation occurs, reducing the need for complex real-time adjustments.
Solution Approach 2:
The system introduces a calibration manager as an intermediary component that coordinates between the gaming application, device resources, and settings updates. This mediator handles the complexity of monitoring multiple state changes and determining appropriate responses, isolating the complexity from both the application and the device resource management.
3Adaptability or versatility
If additional state information is monitored to determine when to update settings, then adaptability is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system extracts and monitors only the most critical state information needed for performance optimization, such as GPU utilization, frame drop rates, and power source state. By focusing on key indicators rather than attempting to monitor all possible device states, the system maintains high adaptability while avoiding the complexity of comprehensive state detection.
Solution Approach 2:
The system monitors changes in specific parameters (such as GPU utilization threshold, frame drop rate threshold, power source state) rather than attempting to measure and analyze all device state continuously. By tracking parameter changes and using these as triggers for calibration events, the system achieves comprehensive adaptability with reduced monitoring complexity.
Data Source
AI summary
In various examples, dynamically calibrating settings for content streaming systems and applications is described herein. Systems and methods are disclosed that monitor information associated with a state of a client device and/or a performance of an application (e.g., an interactive application, etc.) during a session of the application, and then update settings associated with the client device and/or the application during the session in order to optimize a performance of the application. For instance, this information may be used to determine when to update the settings, such as when a GPU utilization satisfies a threshold utilization, a frame drop rate satisfies a threshold rate, and/or the like. In some examples, the settings may then be updated using one or more techniques (e.g., using a decision tree), such as by terminating other applications, updating settings of one or more processors, and/or performing any other procedure.