Cached Content Bridging for Low-Latency Interactive CDN Playback
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Solution Overview
Problem
Content distribution networks face challenges in scaling to serve large numbers of users due to the high computational cost and latency associated with dynamically generating content, particularly for applications like digital humans, which require significant resources and time, making real-time interaction difficult.
Innovation Solution
A cache-based content distribution system that caches source and target content with bridge points, allowing for the dynamic generation of bridges between these cached contents to create seamless transitions, reducing computational requirements and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content is dynamically generated using sophisticated computing techniques, then content quality and personalization are improved, but computational cost and latency increase significantly
Solution Approach 1:
The system pre-generates multiple candidate content portions before user interaction occurs. These candidate portions are stored and ready for rapid retrieval based on actual user events, eliminating the need for real-time generation and reducing latency while maintaining personalization capability.
Solution Approach 2:
Instead of generating unique content for each user interaction, the system creates copies of pre-generated candidate content portions and selects appropriate copies based on user events. This copying approach maintains content quality and personalization while avoiding the computational overhead of real-time generation.
2Manufacturing precision
If multiple deep learning models are used for content generation, then content quality is improved, but device complexity and computational resources required increase
Solution Approach 1:
The content generation task is divided into multiple independent candidate portions, each generated by separate deep learning models. This segmentation allows the system to use sophisticated models for high-quality content while managing complexity through modular, independent generation processes that can be pre-computed and stored.
Solution Approach 2:
The system generates more candidate content portions than strictly necessary (excessive action), allowing the selection process to choose the best matching content based on user events. This approach maintains high content quality through multiple sophisticated model outputs while managing complexity by performing the expensive generation operations in advance rather than in real-time.
3Adaptability or versatility
If content is generated in real-time for each user, then personalization is improved, but scalability to large numbers of users becomes difficult
Solution Approach 1:
Candidate content portions are generated and stored in advance before user interactions occur. When users interact with the system, pre-generated candidate portions are rapidly retrieved and selected based on user events, enabling personalized content delivery to scale across thousands or millions of users without requiring real-time generation for each user.
Solution Approach 2:
The system creates and manages copies of pre-generated candidate content portions for distribution to multiple users. This copying mechanism allows the same high-quality generated content to serve numerous users simultaneously, maintaining personalization through intelligent selection while achieving scalability through efficient resource sharing.
Data Source
AI summary
Computer-implemented content delivery includes caching a source content having a plurality of bridge points. A user event indicating an interaction with a user is received. The user event is received during playback of the source content. In response to the user event, a template is selected from a plurality of cached templates. The template corresponds to a bridge point selected from the plurality of bridge points of the source content as an exit point from the source content. In response to the user event, a bridge is dynamically generated. The bridge links the bridge point with the template. In response to the user event, a target content is selected from a plurality of cached target contents. In response to the user event, the bridge, the template, and the target content is conveyed to a device of the user for playback following the bridge point of the source content.