Dynamic Network Resource Allocation for Mobile Content Delivery
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Solution Overview
Problem
Mobile telecommunication networks face challenges in delivering high-quality content due to limited network resources, leading to congestion, latency, and variable throughput, which affects real-time multimedia services and user experience.
Innovation Solution
A dynamic method for content delivery that predicts and adapts to real-time network capacity needs by monitoring traffic loads, optimizing network configuration, and prioritizing Quality of Service (QoS) based on user profiles, device properties, and traffic patterns, using selected caches and proxy caches to ensure efficient content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If network resources are increased to improve content delivery quality, then service quality improves, but network cost and complexity increase
Solution Approach 1:
The patent implements dynamic resource allocation where network resources are adjusted in real-time based on traffic conditions, user profiles, and service requirements. The system continuously monitors network state and reconfigures resource allocation dynamically, allowing the network to adapt to changing demands without permanent over-provisioning, thus improving delivery quality while controlling complexity.
Solution Approach 2:
The system changes multiple network parameters simultaneously including resource allocation weights, routing decisions, caching strategies, and QoS thresholds based on analyzed traffic patterns and user profiles. This multi-parameter optimization allows the network to achieve high delivery quality through coordinated adjustments rather than simply increasing overall network capacity.
2Productivity
If network monitoring and control mechanisms are enhanced to optimize content delivery, then network efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements self-organizing network behavior where the system automatically analyzes traffic patterns, identifies optimization opportunities, and executes control decisions without external intervention. The network monitors itself, learns from observed patterns, and autonomously adjusts configurations, which improves efficiency while avoiding the complexity of manual control systems or external management infrastructure.
Solution Approach 2:
The system continuously monitors network performance metrics, user experience indicators, and traffic patterns, then uses this feedback to dynamically adjust resource allocation and control parameters. This closed-loop feedback mechanism enables the network to self-optimize based on actual conditions, improving efficiency through data-driven decisions rather than complex predetermined control logic.
3Speed
If content is cached closer to users to reduce latency, then access speed improves, but network infrastructure complexity and cost increase
Solution Approach 1:
The patent implements selective caching where content is cached at specific network locations based on local traffic patterns, user density, and content popularity. Rather than uniformly caching all content at all locations, the system analyzes local conditions and places caches only where they provide maximum benefit, reducing infrastructure complexity while maintaining fast access speeds for relevant users.
Solution Approach 2:
The system proactively caches content before it is requested by predicting future demand based on user profiles, historical patterns, and current traffic analysis. By pre-positioning content at optimal cache locations ahead of time, the system reduces latency when content is actually requested without requiring permanent cache infrastructure at all possible locations, thus balancing speed improvement with infrastructure simplicity.
Data Source
AI summary
A system, method and computer program product for content delivery of services in a mobile telecommunication network on the basis of predictions of user and network needs. The method includes collecting and receiving information of the use of services in the network, and analyzing network behavior on the basis of the information received. The traffic load in the network is continuously monitored in real-time by estimating the traffic load locations in the network and the network capacity needs for delivering the services. The analysis is used for deciding the optimal network configuration to be used for supporting the estimated traffic load locations in the network. The network behavior is controlled based on the selections and decisions of the analysis by sending requests to network components for network configuration.