Content Preloading via Value Metric and Network Type
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Solution Overview
Problem
Users face challenges in accessing content due to size and speed/cost issues with communication networks, leading to unsatisfactory viewing experiences and lost revenue for content providers, as they struggle to preload content efficiently across diverse user devices and networks.
Innovation Solution
A method and system for preloading content onto user devices based on a value metric that includes a cost parameter, managed by content distribution servers and assisted by storage managers, ensuring content is preloaded on appropriate storage elements over communication networks, optimizing resource usage and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If content is preloaded over communication networks, then content availability improves, but bandwidth costs increase
Solution Approach 1:
The system performs preliminary actions by preloading content to storage elements before users actually request it. The content distribution server proactively transfers content based on predicted user needs, network conditions, and storage availability, ensuring content is ready when needed without waiting for user requests.
Solution Approach 2:
The system dynamically changes parameters including network bandwidth allocation, storage selection priorities, and content selection criteria based on a value metric. It adjusts preloading behavior according to network conditions, user preferences, device capabilities, and content characteristics to optimize the balance between availability and cost.
2Ease of operation
If content is preloaded to storage elements, then user satisfaction improves, but network resource usage increases
Solution Approach 1:
The system enables self-service by allowing storage elements and user devices to automatically receive and manage preloaded content based on their specific needs, capabilities, and current state. The content distribution server provides the mechanism, but the actual preloading decisions are made locally based on real-time conditions.
Solution Approach 2:
The system applies partial preloading by selecting only the most valuable content to preload based on user preferences, device storage capacity, and network conditions. It preloads部分内容 rather than all content, optimizing the balance between user satisfaction and network resource consumption.
3Loss of energy
If content selection is based on value metric with cost parameter, then revenue optimization improves, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the content distribution server continuously monitors user behavior, content access patterns, network conditions, and storage status. This feedback informs dynamic adjustments to the value metric calculations and preloading decisions, enabling revenue optimization through data-driven choices.
Solution Approach 2:
The content distribution server performs multiple functions including content management, user profile management, value metric calculation, preloading decision-making, and network resource allocation. This multi-functionality consolidates complexity into a single system rather than requiring separate specialized components.
4Loss of energy
If content is preloaded during off-peak times, then bandwidth costs reduce, but preloading time increases
Solution Approach 1:
The system employs periodic action by scheduling content preloading during off-peak network hours when bandwidth costs are lower. It identifies and utilizes periodic windows of opportunity in network usage patterns to perform preloading operations, balancing cost savings with eventual content availability.
Data Source
AI summary
A content distribution system generates a queue of content for a user account, including content available from a network content server and content that has been preloaded from the network content server to a home content reservoir. A wireless end-user device can request streaming of queue content from either location. At least for content streamed to the wireless end-user device directly from the network content server, the media quality of the streamed content is dependent on a current network type to which the wireless end-user device is connected.