Dynamic Encoding Profiles for Content-Adaptive ABR Streaming
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Solution Overview
Problem
Traditional adaptive bit-rate (ABR) streaming encoders fail to optimize encoding profiles based on factors such as content complexity, geographic reach, network capabilities, and device types, resulting in sub-optimal bit rates.
Innovation Solution
Dynamic profiling techniques that involve probe encodings to determine quality-rate models, allowing for optimization of encoding parameters like bit rates, resolutions, and codecs to achieve desired performance criteria, such as minimizing bandwidth usage while maintaining quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static encoding profiles are used for ABR streaming, then the encoding process is simple and fast, but the bit rates are sub-optimal and do not adapt to content complexity or network conditions
Solution Approach 1:
The patent implements dynamic encoding profiles that automatically adjust bit rates based on content complexity analysis and network conditions. The system transitions from static pre-defined profiles to dynamic profiles that are generated or selected in real-time based on measured parameters such as motion content, scene changes, and detected network bandwidth, thereby achieving both adaptability and maintaining encoding efficiency through automated decision-making.
Solution Approach 2:
The system changes encoding parameters (bit rate, resolution, codec settings) dynamically based on content characteristics and network conditions. By analyzing content complexity metrics and network performance data, the system adjusts encoding parameters to optimize quality-of-experience while adapting to varying conditions, resolving the contradiction between simple static profiles and adaptive profiles.
2Reliability
If higher bit rates are used for all content, then quality is improved, but bandwidth consumption increases and network efficiency decreases
Solution Approach 1:
The patent applies different bit rates to different segments or portions of video content based on their local complexity characteristics. High-motion or complex scenes receive higher bit rates to maintain quality, while low-motion or simple scenes receive lower bit rates, thereby optimizing overall bandwidth consumption while maintaining acceptable quality throughout the video stream.
Solution Approach 2:
Instead of uniformly applying high bit rates to all content, the system applies bit rate allocation selectively based on content needs. By using probe encodings and quality-rate models to determine minimum sufficient bit rates for acceptable quality, the system avoids excessive bandwidth consumption while maintaining quality where necessary.
3Measurement precision
If more probe encodings are performed to determine quality-rate models, then encoding optimization accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs probe encodings and quality-rate model determination in advance during content ingestion or preprocessing phases, rather than in real-time during streaming. This preliminary action allows comprehensive analysis and model building without impacting real-time streaming performance, resolving the contradiction between accurate measurement and time loss.
Solution Approach 2:
The system dynamically adjusts the number and granularity of probe encodings based on content characteristics and available resources. For some content types, fewer probe points may suffice, while for others more detailed analysis is performed. The system balances measurement precision with processing time by adapting the probing strategy to specific content needs.
Data Source
AI summary
Techniques described herein are directed toward creating one or more “dynamic profiles” for media (video) streaming in which an encoding bit rate (and optionally other profile settings) is optimized for particular content. More specifically, techniques involve performing one or more “probe” encodings of the particular content to determine an encoding bit rate (and optionally other profile settings) that results in an encoding having a quality value sufficiently near (within a threshold) a target quality value.