Live Streaming ABR Ladder Optimization With Real-Time Bitrate-Resolution Pairs
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Solution Overview
Problem
Existing adaptive bitrate (ABR) streaming technologies face challenges in efficiently optimizing bitrate-resolution pairs for live media assets due to high computational complexity and cost, especially in low-latency scenarios, as they do not adequately consider content characteristics, leading to coding artifacts and degraded quality of experience (QoE).
Innovation Solution
A computer-implemented system that determines optimal bitrate-resolution pairs in real-time by ingesting a live media asset, using machine learning models to analyze bitstream-level statistics and generate content-dependent ABR ladders, which can be transcoded at edge servers with minimal computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If per-title and shot-optimized encoding is used to build Pareto frontier across rate-distortion curves, then video quality is improved, but computational complexity and cost increase massively
Solution Approach 1:
The system performs optimization in advance by building a Pareto frontier of rate-distortion curves before actual streaming. Pre-computed optimal bitrate-resolution pairs are stored and reused, eliminating the need for real-time optimization calculations during live streaming operations.
Solution Approach 2:
The video content is divided into segments or chunks, and optimization is performed at the segment level rather than requiring full-video processing. This allows parallel processing and reduces the computational burden on any single processing unit.
2Adaptability or versatility
If extensive iterative processing is performed to optimize ABR ladder for each content, then content-specific optimization is achieved, but processing time increases making it prohibitive for live streaming
Solution Approach 1:
Optimization is performed in advance during content ingestion or pre-processing phases. The system analyzes content characteristics and computes optimal bitrate-resolution pairs before the content needs to be streamed, so that when live streaming occurs, the optimization work is already complete.
Solution Approach 2:
The system dynamically adapts the optimization process to content characteristics by analyzing video complexity, motion levels, and scene changes. The optimization parameters and processing intensity are adjusted based on the specific content being processed, allowing efficient handling of different content types.
3Device complexity
If fixed ABR ladder with pre-defined resolutions is used for live streaming, then processing complexity is reduced, but video quality deteriorates due to lack of content-specific optimization
Solution Approach 1:
Different parts of the content receive different optimization treatments based on their local characteristics. Video segments with high motion or complex scenes receive different bitrate-resolution allocations compared to simpler segments, allowing quality optimization without uniform processing complexity.
Solution Approach 2:
The system changes key parameters such as bitrate, resolution, and frame rate based on content characteristics and network conditions. By dynamically adjusting these parameters rather than using fixed values, the system maintains lower processing complexity while improving video quality through adaptive optimization.
Data Source
AI summary
Systems and methods are described for transcoding at least a portion of a live media asset ingested from a media content source. The systems and methods may be configured to, in real time, after ingesting the at least a portion of the live media asset, determine parameters of the at least a portion of the live media asset. The systems and methods may be further configured to, in real time, after ingesting the at least a portion of the live media asset, determine, based on the parameters, a plurality of optimal bitrate-resolution pairs for the at least a portion of the live media asset. The systems and methods may be further configured to, in real time, after ingesting the at least a portion of the live media asset, cause the at least a portion of the live media asset to be transcoded based on the plurality of optimal bitrate-resolution pairs.