Per-Viewer Video Optimization Using Engagement Models
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Solution Overview
Problem
Optimizing video streaming quality for individual viewers is challenging due to varying preferences and behaviors, as existing technologies fail to personalize content delivery based on viewer-specific preferences and real-time performance feedback.
Innovation Solution
Implementing a per-viewer engagement-based video optimization system that uses machine learning models to analyze viewer behavior and preferences, providing personalized instructions for initial bitrate, player buffer length, and content distribution strategies tailored to each viewer's engagement patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video streaming quality is optimized for all viewers using a single approach, then general streaming performance is maintained, but individual viewer engagement and satisfaction deteriorate due to varying preferences and behaviors
Solution Approach 1:
The system segments viewers into different groups based on their behavior patterns and preferences, applying different streaming optimization strategies to each segment. This allows personalized optimization without requiring completely separate systems for each viewer, thus improving adaptability while controlling complexity.
Solution Approach 2:
The system dynamically adjusts streaming parameters (bitrate, resolution, buffer size) based on learned viewer preferences and real-time performance feedback. By changing parameters rather than restructuring the entire system, the patent achieves personalization with manageable complexity.
2Manufacturing precision
If high resolution video is delivered to all viewers, then video quality is improved, but startup time deteriorates due to larger file sizes and longer buffering requirements
Solution Approach 1:
The system dynamically adjusts video quality parameters during playback based on real-time network conditions and viewer preferences. Instead of static high resolution for all, the system adapts resolution and bitrate dynamically, allowing fast startup when needed while maintaining quality when appropriate.
Solution Approach 2:
The patent changes video delivery parameters (resolution, bitrate, format) based on viewer preferences and network conditions. For viewers who prioritize quality, higher resolution is delivered; for those who prioritize speed, lower resolution with faster startup is provided, resolving the contradiction between quality and startup time.
3Reliability
If adaptive bitrate streaming is used to optimize playback smoothness, then buffering issues are reduced, but video quality deteriorates due to compression artifacts and lower resolution
Solution Approach 1:
The system applies different quality levels to different parts of the video stream based on viewer preferences and network conditions. Instead of uniform compression, the patent uses selective quality adjustment, maintaining high quality in important regions while reducing quality in less critical areas, thus balancing smoothness and quality.
Solution Approach 2:
The system uses real-time feedback from network conditions and viewer behavior to adjust bitrate and quality settings. By continuously monitoring playback smoothness and quality metrics, the system can optimize the balance between buffering and quality, preventing degradation while maintaining smooth playback.
4Productivity
If personalized optimization is implemented for each viewer, then engagement is improved, but computational resources and system complexity worsen due to individual model training and analysis
Solution Approach 1:
The system merges data from multiple viewers to create generalized behavior models that can be applied to groups of similar viewers. Instead of training completely separate models for each individual, the patent combines data efficiently, reducing computational resources while still providing personalized optimization through cluster-based approaches.
Solution Approach 2:
The system performs preliminary analysis and model training during periods of low demand, preparing optimization strategies in advance. By doing computational work beforehand rather than in real-time, the patent reduces immediate computational resource usage while still providing personalized optimization when needed.
Data Source
AI summary
Per-viewer engagement-based video optimization is disclosed. A request for content associated with a first client is received. A model associated with the first client is obtained. The obtained model comprises at least one of behavior and playback preferences of a viewer associated with the first client. The obtained model is used to determine, for the first client, an optimal set of instructions usable to obtain content. A different set of instructions is determined to be optimal for a second client. The optimal set of instructions determined for the client is provided as output. The first client is configured to obtain content according to the optimal set of instructions determined for the first client.