Content-Aware Neural Network for Video Delivery
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Solution Overview
Problem
Current internet video delivery infrastructures treat video content as a stream of bits without consideration for content type, leading to inefficient delivery and quality issues, especially in limited network resources.
Innovation Solution
A server apparatus and method using a content-aware neural network for clustering similar content and training cluster-wise content reconstruction models to deliver high-quality content efficiently, even in low network conditions, by compressing and reconstructing content based on similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video content is treated as a stream of bits with uniform delivery technology, then infrastructure simplicity is maintained, but delivery efficiency and quality deteriorate
Solution Approach 1:
The patent segments video content into clusters based on semantic similarity using neural network-based image classification. By dividing the content delivery system into content-specific clusters rather than treating all content uniformly, the system achieves content-aware delivery that improves efficiency while maintaining manageable infrastructure complexity through automated classification.
Solution Approach 2:
The patent applies different delivery strategies to different content clusters based on their specific characteristics. By training separate reconstruction models for each content cluster, the system optimizes delivery quality locally for each type of content rather than using a one-size-fits-all approach, thereby improving overall delivery efficiency.
2Manufacturing precision
If traditional signal processing techniques are used for video encoding, then computational simplicity is maintained, but quality in limited network resources deteriorates
Solution Approach 1:
The patent replaces traditional signal processing techniques with neural network-based image classification and reconstruction models. This substitution enables content-aware processing that achieves superior quality in limited network conditions, with the computational energy investment yielding higher returns through intelligent content understanding and reconstruction.
3Quantity of substance
If content is compressed for bandwidth efficiency, then bandwidth usage is reduced, but reconstruction quality deteriorates
Solution Approach 1:
The patent performs preliminary clustering and model training actions before actual content delivery. By pre-processing content into clusters and training reconstruction models in advance, the system enables efficient compression and high-quality reconstruction during delivery, achieving both bandwidth efficiency and quality preservation through advance preparation.
Solution Approach 2:
The patent uses learned reconstruction models to generate high-quality content copies from compressed representations. The neural networks learn to reconstruct original content from compressed versions, enabling bandwidth-efficient transmission while maintaining reconstruction quality through intelligent copying rather than direct transmission.
Data Source
AI summary
The present disclosure provides a method and a server apparatus for delivering content based on content-aware using a neural network. A server apparatus for content delivery is provided, including a content clustering unit for clustering multiple contents provided from a content provider based on a similarity; a training unit for training a cluster-wise content reconstruction model by using contents contained in each cluster in accordance with a result of clustering performed by the content clustering unit; a storage unit for storing the multiple contents and the cluster-wise content reconstruction model; and a transmission unit for transmitting content requested by a user and a content reconstruction model corresponding to a cluster containing the content requested to a user terminal.