Media Content Pre-positioning via Surplus Network Capacity
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Solution Overview
Problem
Existing methods for delivering media content, such as progressive download, often result in interruptions due to network fluctuations, requiring users to wait for content to download before playback and failing to provide continuous playback sessions when network impairments persist.
Innovation Solution
Systems and methods for predicting popular media content and pre-delivering it to user devices using available surplus network capacity, allowing instant playback without the need for buffering or user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If progressive download with pre-buffering is used, then interruptions during playback are reduced, but users must wait for content to download before playback begins and playback sessions are forced to halt when buffers are emptied
Solution Approach 1:
The system performs preliminary actions by predicting which media content files will become popular before they are widely requested, and pre-delivers these files to user devices in advance. This allows playback to begin immediately without buffering delays and ensures content is already available locally even if network conditions deteriorate during playback
Solution Approach 2:
The system uses automated prediction algorithms that monitor content statistics and user behavior patterns to autonomously determine which files to pre-position, eliminating the need for manual user planning and automatic buffer management, allowing the system to serve itself in optimizing content delivery
2Ease of operation
If pre-delivery of content is implemented, then user experience is improved with continuous playback, but network capacity is consumed before user requests
Solution Approach 1:
The system applies local quality by pre-delivering content selectively to specific user devices based on individual prediction scores and user profiles, rather than universally pre-delivering all content. This optimizes network capacity usage by targeting only those users most likely to consume the predicted popular content
Solution Approach 2:
The system changes parameters dynamically by adjusting prediction thresholds, content selection criteria, and pre-delivery timing based on monitored content statistics, user behavior patterns, and network conditions, allowing optimization of the balance between user experience and network capacity consumption
3Speed
If content is pre-positioned on user devices, then instant playback is enabled without buffering, but network bandwidth is utilized during off-peak periods
Solution Approach 1:
The system performs preliminary content delivery during off-peak network periods when bandwidth is more readily available, transferring media files to user devices before peak usage times. This enables instant playback during high-demand periods while utilizing network capacity when it is less constrained
Solution Approach 2:
The system implements periodic monitoring of content statistics and user behavior patterns to update predictions and trigger pre-delivery actions at appropriate intervals, aligning content transfer operations with network conditions and user consumption patterns to optimize bandwidth utilization
Data Source
AI summary
Systems and methods for determining or “predicting” which media content files are popular or will be popular, and based on that determination, pre-delivering or “pre-positioning” media content files to user devices automatically and without intervention from users so that the media content will be already stored on users' devices when they later select the media content for viewing. The determination of a media content file's popularity may be based on one or more combinations of content statistics (e.g., view count, viewing rate, etc.). The decision to pre-position a media content file may also be based on user profile information (e.g., viewing history). The pre-positioning may be accomplished using transport technology that avoids further burdening the network, such as delivering in real-time using available surplus network capacity.