Screen Sharing Frame Filtering for Bandwidth Spike Reduction
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Solution Overview
Problem
Current virtual conferencing platforms experience bandwidth constraints due to unnecessary data spikes during screen sharing transitions, leading to audio quality degradation for bandwidth-constrained clients.
Innovation Solution
A machine learning model predicts instances of screen sharing content changes by analyzing user interactions and application contextual data, identifying unnecessary video frames to be removed from the stream, thereby optimizing video sharing and reducing data spikes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all video frames are transmitted during screen sharing, then complete visual information is provided to all participants, but bandwidth spikes occur and audio quality degrades for bandwidth-constrained clients
Solution Approach 1:
The system extracts and removes unnecessary video frames from the transmission stream based on interaction intent analysis. By identifying frames that do not contribute to the collaborative task (such as frames during pauses, when participants are not actively viewing, or redundant frames), the system eliminates these frames from transmission, thereby reducing bandwidth consumption while preserving essential visual information for participants.
Solution Approach 2:
The system dynamically changes the video frame transmission parameter (frame rate) based on interaction context. During active collaboration phases, higher frame rates are maintained to preserve visual information. During inactive phases or when interaction intent indicates reduced need for visual updates, the frame rate is reduced, optimizing bandwidth usage while maintaining information completeness when needed.
2Loss of energy
If video frame transmission is reduced to minimize bandwidth spikes, then audio quality is maintained, but visual information may be lost
Solution Approach 1:
The system uses interaction intent analysis as feedback to dynamically control video frame transmission. By monitoring participant interactions, collaboration phase detection, and contextual cues, the system receives feedback about when visual information is actually needed. This feedback loop enables the system to reduce frame transmission during periods when visual information is less critical while maintaining transmission during periods when participants need visual updates, thus preventing information loss while optimizing bandwidth efficiency.
Solution Approach 2:
The system performs preliminary analysis of interaction intent and collaboration context before determining which video frames to transmit. By predicting when visual information will be needed based on interaction patterns and collaboration phase, the system proactively maintains frame transmission during critical moments while reducing transmission during non-critical periods, ensuring visual information completeness is preserved when needed while achieving bandwidth efficiency overall.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed to improve screen sharing based on identification of irrelevant video frames from interactive context. Example apparatus disclosed herein are to process input data and application contextual data to determine an interaction classification for a classification interval in a screen sharing event initiated in a video conference and identify, based on the interaction classification, video frames of the screen sharing event that correspond to the classification interval to exclude from a transport stream for the video conference associated with the classification interval. Disclosed example apparatus are further to exclude the identified video frames from the transport stream and provide remaining video frames of the screen sharing event to a video encoder for inclusion in the transport stream.