Dynamic Content Delivery Prioritization for VOD Availability
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Solution Overview
Problem
In video content distribution systems, content providers often face challenges in ensuring timely processing and delivery of video content to storefronts, leading to missed revenue opportunities and customer dissatisfaction due to unpredictable content availability and finite processing resources.
Innovation Solution
A Dynamic Content Delivery Prioritization (DCDP) system that utilizes a Predictive Content Processing Estimator (PCPE) to estimate content processing times, dynamically reprioritizing content based on remaining delivery time and system conditions, ensuring content is delivered on time for availability windows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content is processed using finite processing resources with traditional prioritization methods, then processing capacity is limited, but content delivery timeliness deteriorates leading to missed availability windows
Solution Approach 1:
The patent implements dynamic prioritization that adjusts content processing priority in real-time based on remaining availability window time and predicted processing duration. Content items are continuously re-evaluated and re-prioritized as they move through the processing pipeline, allowing the system to adapt to changing conditions and ensure time-critical content receives appropriate processing resources.
Solution Approach 2:
The system performs preliminary estimation of processing duration for each content item before full processing begins. This prediction step allows the system to pre-calculate priority assignments and proactively allocate processing resources, preventing missed deadlines rather than reacting after delays occur.
2Reliability
If traditional content prioritization is used, then processing order is static, but content delivery effectiveness worsens due to unpredictable availability window misses
Solution Approach 1:
The patent incorporates feedback loops where the actual processing duration of completed content items is used to refine and improve the accuracy of predictive models for future content. This continuous learning mechanism allows the system to become more accurate over time without requiring proportional increases in system complexity.
Solution Approach 2:
The system automatically performs priority assignment and content scheduling without requiring manual intervention. The automated prioritization engine evaluates content metadata, predicts processing requirements, and assigns optimal processing orders autonomously, reducing operational complexity while improving reliability.
3Reliability
If content processing is expedited for critical items, then delivery timeliness improves, but resource allocation efficiency deteriorates without dynamic prioritization
Solution Approach 1:
The patent changes the priority parameter dynamically based on multiple factors including remaining availability window time, predicted processing duration, and content criticality. This multi-parameter prioritization approach ensures that resource allocation is optimized for each specific content item's needs, expediting only when necessary and avoiding waste on non-critical items.
Data Source
AI summary
Devices, systems, and methods for selectively reordering a plurality of content titles in a queue storing content titles to be delivered to a Video-on-Demand (VOD) server accessible to customers over a content delivery network, and delivered by an availability window start time, by using an estimated time to such delivery.