Adaptive Bitrate Ladder Tuning for Variable-Quality UGC
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing adaptive bitrate (ABR) streaming systems inefficiently allocate bitrate and resolution based on inaccurate metadata, leading to unnecessary resource consumption and poor quality of user-generated content (UGC) delivery, particularly in OTT and IPTV services, due to inconsistent and often low-quality source content.
Innovation Solution
Systems and methods for automatically assessing the quality of UGC by analyzing visual characteristics rather than metadata, determining the highest operating point for each content portion, and optimizing the ABR ladder to minimize resource usage while maximizing quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bitrate and resolution are increased for low-quality source content, then quality metrics improve, but resource consumption increases unnecessarily
Solution Approach 1:
The system performs preliminary quality assessment of the source content before encoding to determine the actual quality level. This preliminary action prevents unnecessary high-bitrate encoding of low-quality source material by establishing the true quality baseline upfront, allowing the ABR ladder to be optimized accordingly without wasting resources on preserving artifacts that cannot be improved.
Solution Approach 2:
The system changes the parameter used for quality determination from metadata-based values to visually-assessed quality metrics. By analyzing actual visual characteristics rather than trusting source metadata, the system adjusts the encoding parameters (bitrate, resolution) to match the true quality level of the source content, preventing unnecessary resource consumption while maintaining appropriate quality.
2Ease of manufacture
If metadata is used for quality determination, then encoding process is simplified, but quality assessment accuracy deteriorates
Solution Approach 1:
The system introduces an intermediary visual quality assessment step between receiving the source content and performing encoding. Instead of directly using metadata for quality determination, the system inserts a visual analysis intermediary that evaluates actual image quality characteristics, providing more accurate quality metrics while maintaining encoding efficiency through automated visual inspection.
Solution Approach 2:
The system replaces the mechanical/met data-based quality assessment method with a visual analysis approach. Instead of relying on metadata fields that may be inaccurate or misleading, the system substitutes this with automated visual inspection of actual content characteristics, achieving more precise quality measurement while keeping the process automated and efficient.
3Measurement precision
If ABR ladder is optimized for high quality, then quality delivery improves, but bandwidth and storage resources are wasted on low-quality content
Solution Approach 1:
The system applies local quality optimization by creating different ABR ladder configurations based on the actual quality level of each source content item. Instead of using a uniform high-quality ABR ladder for all content, the system tailors the bitrate-resolution progression to match each content's true quality characteristics, ensuring high quality delivery where appropriate while reducing resource allocation for lower-quality source material.
Solution Approach 2:
The system implements dynamic ABR ladder optimization that adapts to the quality level of each source content item. Rather than using static encoding parameters, the system dynamically adjusts the ABR ladder configuration based on visual quality assessment results, allowing bandwidth and storage resources to be allocated efficiently according to each content item's actual quality requirements.
Data Source
AI summary
Systems and methods are disclosed for generating an adaptive bitrate ladder for a content item based on the actual quality of the content item. A user uploaded content item is determined to have different quality scores in different time portions of the content item, where the quality score is based on an analysis of visual characteristics of frames of the content item. In response, an adaptive bitrate ladder (ABR) is generated for each time portion which contains a set of bitrates based on the quality score.