Central Analytics Server for RAN-Aware Content Optimization
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Solution Overview
Problem
Conventional mobile edge computing (MEC) lacks quality of service (QoS) and disaster recovery mechanisms, leading to inefficiencies in content delivery and increased latency in congested networks, which is unsuitable for real-time applications.
Innovation Solution
A central analytics server with a global view of the communication network analyzes radio access network (RAN) health data to determine throughput and recommend actions for content servers to optimize content delivery, using in-band and out-of-band signaling to adjust bit streams and select optimal delivery routes, thereby enhancing QoS and supporting disaster recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If content is offloaded to a conventional centralized cloud to conserve UE resources, then device resources are preserved, but execution delay increases significantly
Solution Approach 1:
The patent introduces a new spatial dimension by deploying edge computing servers at the network edge (radio access network) rather than relying solely on centralized cloud or local device processing. This creates an intermediate processing layer that reduces transmission distance and latency while conserving UE resources through task offloading to the edge server.
Solution Approach 2:
The edge computing server acts as an intermediary between the UE and the centralized cloud. It provides local processing capabilities that reduce execution delay for time-sensitive tasks while still allowing complex tasks to be offloaded from UE, thus mediating between device resource conservation and low-latency requirements.
2Speed
If MEC is used to reduce latency, then real-time application performance improves, but QoS and disaster recovery mechanisms are lacking
Solution Approach 1:
The patent implements a feedback mechanism where the edge computing server receives RAN health data and analytics information, processes this data to determine current network conditions, and adjusts its content delivery and task execution strategies accordingly. This feedback loop enables dynamic QoS management and informed disaster recovery decisions based on real-time network status.
Solution Approach 2:
The system performs preliminary actions by proactively analyzing RAN health data and predicting potential network failures or congestion. Edge servers pre-position content and compute resources in anticipation of demand, and establish backup pathways before disasters occur, enabling faster response and recovery when issues arise.
3Productivity
If content caching and context awareness mechanisms are employed, then service providing ability improves, but system complexity increases
Solution Approach 1:
The patent segments the complex MEC system into distinct functional modules: RAN health data collection, analytics processing, content caching management, and context awareness. Each module performs a specific function and can be independently managed, which reduces overall system complexity while maintaining high service providing ability through coordinated operation of these specialized components.
Data Source
AI summary
A central analytics server can be utilized to analyze health data associated with one or more radio access networks (RANs) that has been aggregated from one or more mobile edge computing (MEC) servers, to determine throughput available at a radio link interface associated with a user equipment (UE). Further, the central analytics server can determine, based on the available throughput, a recommendation for an action that can be performed by a content server to optimize content delivery to the UE. In an example, the central analytics server can convey the recommendation to the content server(s) via a “window size” field within a header segment of a transmission control protocol (TCP) via in-band and/or out-of-band signaling. In one aspect, the recommendation can comprise instructions to adapt a bit stream and/or comprise instructions indicative of an optimal data transmission route.