Video Encoding Ladder Assignment via Content Clustering
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Solution Overview
Problem
Determining an appropriate encoding ladder for diverse video content in streaming services is challenging, as default encoding ladders may not suit all content types, leading to sub-optimal quality and bitrate issues.
Innovation Solution
The system clusters video samples based on optimal convex hulls to select representative encoding ladders, generates feature sets for training classification models, and assigns new samples to appropriate clusters for optimized encoding, using encoding and spatio-temporal features to predict the best encoding ladder for each input video sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a default encoding ladder is used for all video content, then the encoding process is simple and fast, but the encoding quality is sub-optimal for diverse content types
Solution Approach 1:
The patent segments the video content catalog into different clusters based on content characteristics (e.g., sportscasts, news, entertainment). Each cluster is assigned a tailored encoding ladder optimized for its specific content type, rather than using a single default encoding ladder for all content. This segmentation allows high-quality optimized encoding for each content type while maintaining overall system efficiency.
Solution Approach 2:
The patent applies local quality by creating content-specific encoding ladders for different video categories. Each cluster receives a customized encoding ladder with parameters optimized for its particular characteristics (e.g., higher bitrates for sportscasts with fast motion, different resolutions for news vs. entertainment). This ensures optimal encoding quality for each local content type rather than uniform treatment.
2Manufacturing precision
If tailored encoding ladders are created for different content types, then encoding quality is improved, but the system complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-clustering video content and pre-determining optimal encoding ladders for each cluster before actual encoding occurs. The system analyzes content characteristics upfront, groups similar content together, and assigns encoding ladders in advance. This preliminary preparation reduces real-time complexity during the encoding process itself.
Solution Approach 2:
The patent uses copying by creating representative encoding ladders for each content cluster that can be reused across multiple similar videos. Instead of creating unique encoding configurations for every single video, the system creates template encoding ladders for each cluster type and applies them to all videos within that cluster, reducing overall system complexity.
3Productivity
If content is clustered and analyzed to determine optimal encoding ladders, then encoding efficiency is improved, but computational intensity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional classification model that serves multiple purposes: it clusters content, identifies content characteristics, and assigns encoding ladders all in one process. The same feature extraction and classification infrastructure is used both for initial clustering and for ongoing video assignment, reducing redundant computation and overall computational intensity.
Data Source
AI summary
Techniques are described for optimizing and assigning video encoding ladders.