Content Distribution System Using Utility Prediction
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Solution Overview
Problem
Existing content distribution mechanisms are inefficient in delivering personalized content, leading to suboptimal network usage and inability to meet peak demand, resulting in unsatisfied user requests and increased service provider costs.
Innovation Solution
A method and apparatus that utilize content utility prediction information to select the most efficient distribution mode, destination nodes, and propagation times for content items, optimizing network capacity by prioritizing, sequencing, and scheduling content delivery using broadcast, switched broadcast, multicast, and unicast modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content is distributed using traditional mechanisms without optimization, then user requests can be fulfilled, but network efficiency deteriorates and service provider costs increase
Solution Approach 1:
The system performs preliminary actions by predicting content utility and demand before actual content distribution occurs. Content utility prediction information is gathered and analyzed in advance, allowing the system to pre-determine optimal distribution modes, destination nodes, and propagation times. This proactive approach enables the network to efficiently allocate resources and distribute content only when and where it is most needed, thereby improving network efficiency while reducing unnecessary energy consumption and service provider costs.
2Productivity
If content distribution is optimized using prediction information, then network efficiency improves, but system complexity increases
Solution Approach 1:
The system introduces content utility prediction information as an intermediary element that mediates between content sources and destination nodes. This prediction information serves as a guiding mechanism that simplifies the distribution decision-making process by providing pre-analyzed utility metrics. Instead of implementing complex real-time analysis at each network node, the system uses this intermediary prediction data to straightforwardly select distribution modes and target nodes, thereby achieving improved network efficiency without proportionally increasing system complexity.
3Reliability
If multiple distribution modes are used based on utility prediction, then ability to satisfy user requests increases, but distribution complexity increases
Solution Approach 1:
The system applies local quality by selecting different distribution modes (broadcast, multicast, unicast) based on the specific utility prediction characteristics of each content item and its intended destination nodes. Rather than using a single uniform distribution approach, the system tailors the distribution method to local conditions - using broadcast for high-utilitarian content, multicast for moderate utility, and unicast for specific low-utility requests. This localized adaptation improves the ability to satisfy user requests while keeping distribution complexity manageable through rule-based selection rather than global optimization.
Data Source
AI summary
The invention includes a method and apparatus for delivering content to one or more content destination nodes. A method includes receiving content utility prediction information for a content item, selecting a content distribution mode for the content item using the content utility prediction information, and propagating the content item toward at least one of the content destination nodes using the selected content distribution mode and, optionally, with a defined priority, sequence, or schedule. The content utility prediction information is associated with the content destination nodes, and is indicative of a level of utility of the content item to the content destination nodes. The content distribution mode may include any content distribution mode, such as broadcast, switched broadcast, multicast, unicast, and the like. The content utility prediction information is received from one or more content prediction nodes. The content is distributed to one or more content destination nodes, which may include end user terminals and/or network-based caching nodes.