Server-Side Traffic Analysis for Online Collaboration Content Detection
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Solution Overview
Problem
Current online collaboration systems face challenges in accurately detecting and managing different types of content, such as text and video, during online collaboration sessions, often resulting in incorrect recognition and inefficient encoding policies, which affect rendering quality and bandwidth usage.
Innovation Solution
Implementing a server-side Artificial Intelligence (AI) traffic analysis component that analyzes data streams without decoding them, using characteristics like package size and count to classify content types, allowing for optimized encoding settings to be sent to client devices, thereby improving content recognition accuracy and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a server decodes data streams to detect content types, then content recognition accuracy is improved, but computational burden and processing time increase
Solution Approach 1:
The patent extracts only the necessary metadata from data streams (packet size, packet count, timing information) without decoding the actual content. This allows content type classification to be performed on summarized characteristics rather than full data decoding, significantly reducing computational requirements while maintaining recognition accuracy.
Solution Approach 2:
Instead of processing the original data streams directly, the system creates a simplified representation (copy) of the data characteristics - specifically packet-level metadata - for analysis. This copy contains sufficient information for content type detection without requiring full stream decoding, thereby reducing computational burden.
2Device complexity
If uniform encoding policies are applied to all content types, then system complexity is reduced, but rendering quality and bandwidth efficiency deteriorate
Solution Approach 1:
The system transitions from uniform encoding to localized encoding strategies by first classifying content types (text, video, audio) at the stream level. Each content type then receives tailored encoding parameters - text gets optimized for sharpness and clarity, video gets optimized for motion smoothness - thereby improving rendering quality without requiring complex per-pixel processing.
Solution Approach 2:
The encoding policy becomes dynamic rather than static. The system continuously monitors data stream characteristics, classifies content types in real-time, and adjusts encoding parameters accordingly. This dynamic adaptation allows the system to optimize rendering quality for each content type while maintaining manageable system complexity through automated decision-making.
3Speed
If text content is encoded with high motion speed, then video performance is improved, but text clarity and word edge sharpness deteriorate
Solution Approach 1:
The system applies different encoding quality levels to different content types within the same stream. When text content is detected, the system switches to text-optimized encoding parameters that prioritize sharp word edges and clarity, even if this means lower frame rates compared to video content. This local optimization ensures that text remains legible while video maintains its motion smoothness.
Data Source
AI summary
Content determination, in an online collaboration environment, moved from each client to a server, where the server reviews signal characteristics to determine the content in the data stream, may be provided. First, an online collaboration server receives a data stream associated with an online collaboration session. A traffic analyzer at the frontend of the server analyzes the data stream to determine a signal characteristic. Based on the signal characteristic, the traffic analyzer generates a classification of a type of content in the data stream. This classification is sent to an online collaboration application at the client. The client can then adjust the encoding of the data based on the type of content in the data stream.