Predictive Bitrate Ladder for Adaptive Streaming
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Solution Overview
Problem
Existing adaptive streaming technologies require extensive trial encodes for content-aware encoding, which is computationally wasteful and time-consuming.
Innovation Solution
The implementation of a predictive encoding system that uses a prediction engine to determine new encoding settings based on metadata generated during the encoding process, optimizing bitrate and quality for different resolutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If content-aware encoding uses extensive trial encodes to determine appropriate encoding parameters, then encoding precision is improved, but computational waste and time consumption increase
Solution Approach 1:
The system performs preliminary encoding at a first resolution to generate metadata, which is then used to predict optimal encoding parameters for second resolution. This preliminary action avoids the need for extensive trial encodes at the final resolution, reducing computational waste while maintaining encoding precision through informed parameter selection.
Solution Approach 2:
Metadata generated from encoding at the first resolution serves as an intermediary element that bridges the gap between initial encoding and final optimal parameter determination. This metadata is processed by a prediction engine to derive encoding parameters for the second resolution, eliminating the need for direct trial encodes and reducing computational energy consumption.
2Manufacturing precision
If content-aware encoding uses extensive trial encodes to determine appropriate encoding parameters, then encoding precision is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary encoding at a first resolution to generate metadata, which is then used to predict optimal encoding parameters for second resolution. This preliminary action significantly reduces the time required for determining encoding parameters compared to extensive trial encodes, while maintaining precision through prediction-based parameter optimization.
Solution Approach 2:
Metadata generated from encoding at the first resolution serves as an intermediary that enables rapid prediction of optimal encoding parameters for the second resolution. This intermediary approach eliminates time-consuming trial encodes while preserving encoding precision through the prediction engine's parameter derivation.
3Adaptability or versatility
If multiple versions of media content are encoded at different resolutions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The encoding process is segmented into two stages: first, encoding at a lower resolution to generate metadata; second, using a prediction engine to determine parameters for encoding at the target resolution. This segmentation reduces the complexity of generating multiple resolution versions while maintaining adaptability, as the system only needs to manage two encoding passes rather than extensive trial encodes for each resolution.
Solution Approach 2:
Metadata serves as an intermediary that simplifies the encoding system's complexity by enabling parameter prediction for multiple resolutions based on a single initial encoding. This approach maintains high adaptability across different resolutions while reducing the operational complexity of managing multiple encoding processes.
Data Source
AI summary
Methods, systems, and apparatuses may encode a media content item based on metadata from previous encoding. The encoding may also generate encoding metadata, which may comprise a qualitative or quantitative characterization of the encoded media content item. A prediction engine may, based on this metadata, determine new encoding settings for the same or a different video resolution. The prediction engine may cause an encoded media content item to be stored and may cause encoding of the media content item using the new encoding settings.