Dynamic Multicast Channel Provisioning for Network Resource Optimization
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Solution Overview
Problem
Current network operators face inefficiencies in managing network resources due to inaccurate prediction and slow response in determining content consumption trends, leading to suboptimal network performance and user experience, especially during events or sudden increases in content popularity.
Innovation Solution
A system that utilizes a combination of internal data analytics and external trending data to dynamically provision and schedule multicast data channels, leveraging eMBMS technology for efficient content delivery, by analyzing user behavior and social networking data to anticipate and prepare for content requests, thereby optimizing network utilization and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If multiple copies of the same content are delivered to multiple users in a service area, then each user receives the content individually, but the network throughput decreases and network resources are wasted
Solution Approach 1:
The patent merges multiple individual content delivery requests into a single multicast transmission. When the system detects that multiple users in the same service area are requesting the same content, it combines these requests and delivers the content through a single multicast data channel, thereby improving network throughput while still serving all individual users.
Solution Approach 2:
The patent implements a universal content delivery mechanism that can operate in both unicast and multicast modes. The system dynamically selects the appropriate delivery method based on the number of users requesting the same content, making the content delivery system multi-functional and adaptable to different scenarios.
2Productivity
If the network operator monitors network load in real-time to manage channel efficiency, then network performance can be optimized, but the response time to detect content consumption trends is delayed
Solution Approach 1:
The patent performs preliminary actions by proactively predicting content consumption trends using social networking data and analytics before actual content requests surge. The system analyzes trending topics, events, and user behavior patterns to anticipate which contents will be popular, pre-provisions multicast channels accordingly, and prepares content delivery before the demand spike occurs, thereby reducing response time while maintaining performance optimization.
Solution Approach 2:
The patent implements a feedback mechanism that continuously monitors social networking data, user behavior, and content consumption patterns. This feedback loop enables the system to detect emerging content trends earlier than traditional real-time monitoring, allowing for more timely and accurate predictions of content demand.
3Productivity
If the system proactively anticipates content requests using social networking data and analytics, then content delivery efficiency improves, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary content provisioning system that acts as a mediator between social networking data sources and the content delivery network. This intermediary layer analyzes social networking data, predicts content trends, and translates these insights into network provisioning decisions, thereby improving content delivery efficiency while isolating the complexity from the core content delivery infrastructure.
Solution Approach 2:
The system performs preliminary analysis of social networking data and user behavior patterns to predict content consumption trends before actual content requests occur. By pre-processing and pre-analyzing this data, the system establishes prediction models in advance, which simplifies real-time decision-making during content delivery operations.
4Productivity
If multicast channels are dynamically provisioned based on predicted content trends, then network resource utilization improves, but the accuracy of content consumption prediction may be insufficient
Solution Approach 1:
The patent employs parameter changes by adjusting the weight and importance of different data sources (social networking data, user behavior patterns, historical content consumption) in the prediction model. The system dynamically modifies these parameters based on the specific context, content type, and service area characteristics, allowing for more accurate predictions that are tailored to different scenarios and improving overall prediction precision.
Solution Approach 2:
The patent implements local quality by customizing prediction models for different service areas, content types, and user demographics. Rather than using a single uniform prediction approach, the system adapts the prediction parameters and data sources to local conditions, thereby improving prediction accuracy for each specific context while maintaining efficient network resource utilization across the entire network.
Data Source
AI summary
An approach for implementing a content provisioning platform for accurate provisioning of one or more dynamic multicast data channels (e.g., eMBMS) for initiating multicast transmission of contents. The approach includes analyzing content usage data to determine content consumption trend data associated with a topic in a service area. The approach also includes selecting a content package based on the topic, the content consumption trend data, or a combination thereof. Additionally, the approach includes provisioning a dynamic multicast data channel for initiating a multicast transmission of the content package to a plurality of devices in the service area.