AI Transcoding Models for CDN Edge Nodes
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Solution Overview
Problem
The complexity of adding content to a content distribution network (CDN) leads to increased overhead, delays, and potential human errors, and the quality of content received from publishers may be lower than what is distributed via the CDN.
Innovation Solution
The use of distributed ledger and AI-based transcoding technologies to evaluate and apply appropriate transcoding models to content received from content sources, ensuring it meets CDN quality standards and conserving resources by transcoding at edge nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is received from content sources and processed through traditional CDN workflows, then content distribution is achieved, but overhead, delays, and potential human errors increase
Solution Approach 1:
The patent replaces manual CDN workflow processes with automated AI-based systems. AI models automatically evaluate content quality, select appropriate transcoding models, and manage content distribution, eliminating human intervention and reducing operational complexity while improving delivery efficiency.
Solution Approach 2:
The system enables self-service through automated content evaluation and transcoding model selection. The AI-based content processor independently assesses content quality metrics, selects optimal transcoding parameters, and manages the content lifecycle without requiring manual CDN administrator intervention.
2Manufacturing precision
If content from content sources is used directly, then distribution is simplified, but content quality may be lower than CDN standards
Solution Approach 1:
The patent applies preliminary action by evaluating content quality and selecting transcoding models before content distribution. The AI-based content processor assesses content metrics in advance and pre-processes content through appropriate transcoding models to ensure it meets CDN quality standards before being served to consumers.
Solution Approach 2:
The system changes content parameters through AI-driven transcoding models that adjust video and audio characteristics. The content processor modifies resolution, frame rate, bit rate, and other parameters based on the original content analysis and selected transcoding models to achieve target quality levels.
3Manufacturing precision
If high-quality transcoding is applied to all content, then content quality is improved, but CDN resources are consumed excessively
Solution Approach 1:
The patent applies local quality by tailoring transcoding parameters to specific content characteristics. The AI-based content processor analyzes individual content properties (video type, complexity, original quality) and selects appropriate transcoding models that provide sufficient quality improvement without unnecessary processing for content that already meets standards.
Solution Approach 2:
The system applies partial action by selectively applying transcoding only when and where needed. The AI evaluation framework determines the minimum necessary processing level required to meet CDN quality standards, avoiding excessive transcoding for content that already satisfies requirements and optimizing resource utilization.
Data Source
AI summary
Examples of the present disclosure relate to content delivery using distributed ledger and AI-based transcoding technologies. In examples, content is received by a content distribution network (CDN) from a content source. The content may be in a lower-quality or different format than is distributed via the CDN. Accordingly, a transcoding model is identified and used to transcode the content. Multiple transcoding models may be used for different content parts to adapt to changing content types (e.g., a sports subpart, an advertising subpart, etc.). Transcoding may occur at edge nodes, such that the original content is transmitted within the CDN, thereby conserving resources. Additionally, transcoded content may be cached, such that the content need not be transcoded in response to every request.