Video Transcoding Configuration for Timing-Constrained Profile Delivery

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Video delivery systems face challenges in meeting timing requirements for transcoding video asset files due to unpredictable transcoding times, which can result in profiles not being available at the required time or inefficient use of computing resources.

Innovation Solution

A video delivery system predicts transcoding timing for video asset files by analyzing features and using machine learning to optimize the transcoding process, selecting appropriate computing resources and adjusting transcoding settings to ensure profiles are ready within the specified time frame.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the video delivery system transcodes video asset files into multiple profiles, then the system provides more playback options and adaptability for different client conditions, but the transcoding time increases and may not meet timing requirements

Engineering Contradiction:

Improveplayback optionsVSAvoidtranscoding time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of video asset files to predict transcoding timing before actual transcoding begins. This allows the system to identify files that will exceed timing requirements and adjust their transcoding profiles accordingly, ensuring that only feasible profiles are attempted within the required time window.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts transcoding configurations based on predicted timing. If a profile is predicted to take too long, the system automatically modifies transcoding parameters (such as resolution, bitrate, or codec complexity) to reduce processing time while still meeting the timing requirement.

Inventive Principle:

Principle #15Dynamics

2Reliability

If the system predicts and optimizes transcoding timing, then profiles can be delivered on time, but the device complexity and processing overhead increase

Engineering Contradiction:

Improvetiming requirement fulfillmentVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system uses machine learning models that automatically learn from historical transcoding data and improve predictions over time without requiring complex manual configuration. The system self-optimizes by identifying patterns in video content characteristics and their impact on transcoding time, reducing the need for external intervention and simplifying operational complexity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback loops where actual transcoding results are compared with predicted timing, and this information is used to refine future predictions. This continuous learning process improves reliability over time while the feedback mechanism itself remains a relatively simple computational process.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If the system analyzes video features to predict transcoding time, then more accurate timing estimation is achieved, but the preprocessing time and computational resources increase

Engineering Contradiction:

Improvetranscoding time estimation accuracyVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system extracts only the most relevant features from video asset files for timing prediction, such as resolution, duration, and basic content characteristics, rather than analyzing all possible video properties. This selective feature extraction maintains prediction accuracy while significantly reducing the computational resources required for the analysis.

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If the system adjusts transcoding configurations to meet timing requirements, then productivity and on-time delivery improve, but the manufacturing precision and quality may be compromised

Engineering Contradiction:

Improvetranscoding throughputVSAvoidprofile quality

Core Design Contradiction:

ProductivityVSManufacturing precision

Solution Approach 1:

The system intelligently adjusts transcoding parameters such as resolution, bitrate, and codec settings based on the predicted timing and requirements. Rather than using a one-size-fits-all approach, the system selects optimal parameter combinations that balance quality and speed for each specific video file and profile, maintaining manufacturing precision while improving productivity.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12519962B2Optimized video transcoding based on a timing requirement

Publication Date: 2026.01.06 HULU LLC

AI summary

In some embodiments, a method receives a file for transcoding. A timing requirement is associated with the transcoding of the file. The method generates a prediction of a timing estimation based on characteristics of the file and a set of target configurations. A target configuration in the set of target configurations is associated with a transcoding setting. The timing estimation is analyzed for the set of target configurations and the timing requirement to determine a transcoding configuration of a transcoding system. The transcoding configuration of the transcoding system is output to configure the transcoding system to transcode the file. The file is to be transcoded using at least a portion of the set of target configurations based on configuring of the transcoding system.