Edge Device Content Delivery Optimization in MEC
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Solution Overview
Problem
In multi-access edge computing (MEC) environments, resources are inefficiently used when delivering content that is not latency-sensitive, such as streaming video, as far edge and edge clouds are utilized for all types of content, leading to unnecessary resource usage and suboptimal allocation.
Innovation Solution
An edge device determines the optimal edge device to deliver multicast or broadcast content based on the number of user devices and latency requirements, optimizing resource allocation by selecting the appropriate cloud level (far edge, edge, or central cloud) for content delivery, and enabling efficient multicast or broadcast transmissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If far edge and edge clouds are utilized for all types of content delivery, then content delivery coverage is improved, but resource usage efficiency deteriorates
Solution Approach 1:
The patent applies local quality by assigning different content types to different cloud levels based on their specific characteristics. Latency-sensitive content (e.g., AR/VR, real-time gaming) is delivered from far edge or edge clouds, while non-latency-sensitive content (e.g., streaming video) is delivered from central cloud. This differentiated approach ensures that resources are not wasted on delivering non-critical content from edge locations, thereby improving resource usage efficiency while maintaining comprehensive content delivery coverage.
2Loss of time
If edge devices are used for all content delivery, then latency is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the delivery location parameter based on content characteristics and network conditions. For latency-sensitive content, the system changes the delivery parameter to far edge or edge cloud locations. For non-latency-sensitive content, the delivery parameter is changed to central cloud location. This dynamic parameter adjustment optimizes both latency performance and resource allocation efficiency across different content types.
3Reliability
If cloud resources are allocated for all content types at edge levels, then content delivery performance is improved, but resource utilization deteriorates
Solution Approach 1:
The patent applies segmentation by dividing content into distinct categories based on latency sensitivity requirements. Content is segmented into latency-sensitive (AR/VR, real-time interactive applications) and non-latency-sensitive (streaming video, bulk data transfer) types. Each segment is then routed to appropriate cloud levels: latency-sensitive content to far edge/edge clouds for high performance, and non-latency-sensitive content to central cloud. This segmentation ensures reliable content delivery performance for critical applications while preventing waste of edge resources on non-critical content.
Data Source
AI summary
An edge device may obtain, via a base station, one or more respective requests from one or more user devices to access content. The edge device may determine a total number of the one or more user devices and may determine that the total number of the one or more user devices satisfies a threshold. The edge device may determine, based on the total number of the one or more user devices satisfying the threshold, a latency requirement associated with the content and may determine whether the edge device can satisfy the latency requirement. The edge device may selectively cause, based on determining whether the edge device can satisfy the latency requirement, the edge device or a different edge device to send the content to the one or more user devices via the base station.