Content Prioritization System for MVPD Processing Latency
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Solution Overview
Problem
Multichannel Video Programming Distributors (MVPDs) face challenges in prioritizing content processing, leading to delayed content availability and operational inefficiencies, as existing solutions lack the ability to prioritize premium content and optimize resource utilization, resulting in increased latency and revenue losses.
Innovation Solution
A method and system that utilize a content prioritization system to determine premium quality, complexity, and social media vectors for multimedia content segments, assigning weightages using a machine learning model to prioritize content processing based on these factors, ensuring early availability of popular and complex segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If just-in-time processing is used to reduce cost, then operational cost is reduced, but content processing time increases and Service Level Agreement compliance deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-packaging content segments before they are actually requested. The content processing system identifies, processes, and packages content segments in advance based on priority criteria, so that when a viewer requests content, it is already prepared or can be quickly prepared, thereby reducing both processing time and operational cost through efficient resource utilization.
2Device complexity
If sequential content processing is used, then resource management is simplified, but productivity and resource utilization efficiency deteriorate
Solution Approach 1:
The system segments content into multiple independent segments that can be processed in parallel. Each content segment is identified, prioritized, and processed separately by different processing resources simultaneously, rather than processing content sequentially. This segmentation enables efficient parallel processing while maintaining manageable resource allocation through priority-based scheduling.
Solution Approach 2:
The system implements dynamic resource allocation where processing resources are dynamically assigned to different content segments based on real-time priority assessments. The resource management adapts to changing conditions by adjusting which segments receive processing attention, enabling both high productivity and flexible resource management without excessive complexity.
3Ease of operation
If all content segments are processed equally, then processing fairness is maintained, but premium content availability and revenue generation deteriorate
Solution Approach 1:
The system applies different processing qualities and priorities to different content segments based on their characteristics. Premium content segments are identified and assigned higher processing priority, receiving preferential treatment in terms of processing speed and resource allocation, while standard content segments receive normal processing. This local differentiation ensures premium content is made available faster without completely neglecting other content.
4Quantity of substance
If just-in-time packaging is used, then storage and bandwidth efficiency are improved, but video playback latency increases
Solution Approach 1:
The system performs preliminary packaging actions on content segments before they are requested, preparing them in advance for faster delivery. By pre-processing and pre-packaging segments based on priority, the system reduces the time required for packaging at playback time, thereby reducing video playback latency while still maintaining storage efficiency through selective pre-packaging of only high-priority segments.
Data Source
AI summary
Disclosed herein is method and system for prioritizing content for content processing by Multichannel Video Programming Distributors (MVPD). The system upon receiving multimedia content, determines premium quality vector for multimedia content. Further, system segments multimedia content into one or more segments and determines a complexity vector and social media vector for each segment. The complexity vector is identified based on scene transitions and complexity of scene in the segment. The social media vector is identified based on trend, popularity and viral nature of segment. The system assigns weightage to each segment using a machine learning model based on complexity vector, social media vector and premium quality vector. Based on weightage, system prioritizes segments for content processing. In this manner, present disclosure prioritizes content which needs to be first processed to reduce overall content processing time.


