AI Transcoding and Distributed Ledger for CDN Content Delivery
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Solution Overview
Problem
The complexity of adding content to a content distribution network (CDN) leads to increased overhead and potential human errors, and the quality of content received from publishers may be lower than what is available through the CDN, resulting in suboptimal consumer experience due to lower resolutions or fewer audio channels.
Innovation Solution
The integration of distributed ledger and AI-based transcoding technologies, where a content hash is generated and stored in a distributed ledger, enabling smart contracts for CDN analytics and payments, and AI-based transcoding techniques are used to upscale lower-quality content to higher resolutions, such as from HD to 4K or 8K, without requiring content sources to invest in expensive equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is added to CDN through traditional manual processes, then content can be distributed, but overhead increases and human errors are introduced
Solution Approach 1:
The system enables self-service content addition through automated smart contracts that execute automatically when content is uploaded. The distributed ledger automatically records content hashes, manages licensing, and processes payments without manual intervention, reducing overhead and human errors while maintaining high distribution efficiency
Solution Approach 2:
Manual mechanical processes for content validation, licensing, and payment are replaced with automated digital systems. Smart contracts use programmable logic instead of manual administrative processes, and the distributed ledger provides automated record-keeping, eliminating human errors and reducing operational complexity
2Ease of operation
If content from publishers is used directly, then distribution is simplified, but content quality is lower (lower resolution, fewer audio channels)
Solution Approach 1:
The system performs preliminary content validation and quality assessment using automated tools before distribution. Content is pre-checked against quality thresholds and metadata is verified in advance, ensuring that only quality-appropriate content enters the distribution pipeline while maintaining operational simplicity
Solution Approach 2:
An automated quality assessment intermediary layer is introduced between content sources and the CDN. This intermediary uses machine learning models and automated validation tools to bridge the quality gap, evaluating content quality and managing the transformation process without requiring manual intervention or complex infrastructure changes
3Manufacturing precision
If AI-based transcoding is applied to upscale content, then content quality improves (HD to 4K/8K), but processing time and computational resources increase
Solution Approach 1:
The transcoding system dynamically adjusts processing parameters based on content characteristics and requested quality levels. The AI models adapt their processing intensity to the specific content type and resolution requirements, applying more computational resources to high-quality outputs when needed while using minimal processing for standard quality, thus reducing overall processing time
Solution Approach 2:
The system changes processing parameters adaptively based on content analysis. Machine learning models evaluate content characteristics and adjust transcoding parameters such as processing depth, computational complexity, and output resolution dynamically, optimizing the balance between quality improvement and processing time for each specific content item
4Extent of automation
If smart contracts are used for CDN analytics and payments, then payment processing is automated, but system complexity increases
Solution Approach 1:
Smart contracts serve multiple functions simultaneously: content validation, licensing management, analytics tracking, and payment processing. This multi-functionality consolidates what would otherwise require separate complex systems into a single unified smart contract framework, reducing overall system complexity while maintaining high automation
Solution Approach 2:
The distributed ledger creates an immutable copy of all transactions and content metadata, eliminating the need for complex centralized verification systems. This copying mechanism provides automated trust and validation across all parties without requiring complex authentication infrastructure, simplifying the overall system architecture
Data Source
AI summary
Examples of the present disclosure relate to content delivery using distributed ledger and AI-based transcoding technologies. In examples, a content distribution network (CDN) receives content from a content source for distribution to client devices. A content record is generated for the content, which comprises a content hash for the content. The content record may be stored in a distributed ledger. A smart contract associated with the content may be generated, where the smart contract facilitates CDN analytics or accounting for CDN service payments, licensing fees, or royalty payments, among other examples. The smart contract may be associated with the content hash and may be executed based on any of a variety of triggers, such as content playback and/or distribution via the CDN.