Adaptive Encoding Ladder Generation for Streaming Video
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Solution Overview
Problem
Streaming video services face challenges in determining optimal encoding profiles for diverse content characteristics, especially with codecs having numerous encoding parameters, leading to inefficient manual processes and limited automation in optimizing video quality and bit rate.
Innovation Solution
The technique involves adaptive sampling of video content, prioritization of encoding parameters, data space pruning, and optimizing cost functions based on system constraints to generate an adaptive encoding ladder tailored to specific content characteristics, applicable to a wide range of encoding parameters and scalable for various video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If human subject matter experts perform trial and error analysis to determine encoding profiles, then encoding quality can be optimized, but the process becomes time-consuming and cannot scale to large media content catalogs
Solution Approach 1:
The patent replaces manual trial-and-error analysis by human experts with an automated computational system that uses machine learning models and algorithms to determine optimal encoding profiles. This substitution of mechanical human analysis with automated computational processes enables scaling to large media content catalogs while maintaining encoding quality optimization.
Solution Approach 2:
The system enables encoding profiles to be automatically determined through self-service computational processes. The automated system uses machine learning models to independently analyze content characteristics and generate optimized encoding profiles without requiring human expert intervention, thereby reducing time loss while maintaining quality.
2Extent of automation
If computational modules are used for search and optimization processes, then automation is improved, but they are typically limited to only one or two encoding parameters
Solution Approach 1:
The patent creates a universal computational framework that can simultaneously optimize multiple encoding parameters (30-50 parameters) across different video codecs. The system is designed to be codec-agnostic and parameter-agnostic, enabling it to adapt to various encoding standards and optimize comprehensive sets of parameters rather than being limited to one or two specific parameters.
Solution Approach 2:
The system dynamically adapts to different encoding scenarios and parameter sets. It uses machine learning models that can be configured to optimize any number of encoding parameters based on the specific codec and content characteristics, making the automation versatile and adaptable rather than fixed to a limited parameter set.
3Ease of manufacture
If a fixed set of encoding profiles is used for all video content, then the encoding process is simplified, but it cannot accommodate diverse content characteristics and playback conditions
Solution Approach 1:
The patent implements local quality optimization by generating encoding profiles that are specifically tailored to the characteristics of individual video content segments. Instead of applying a uniform encoding profile to all content, the system analyzes local content characteristics (such as scene complexity, motion patterns, and content type) and generates optimized encoding parameters specific to each segment, thereby accommodating diverse content characteristics while maintaining encoding simplicity through automation.
Data Source
AI summary
Techniques are described for optimizing streaming video encoding profiles.