Capgemini Telecommunications Europe

6G-XR project consortium

IT Operations Automation

The consortium needed a way to orchestrate cloud-edge resources for holographic and XR services whose latency and bandwidth demands exceed what current networks can reliably handle.

a multi-site European testbed platform
infrastructures in Finland and Spain
two complete holographic communication channels

Reported impact

Business outcome

The consortium proved that AI-driven edge orchestration can keep immersive communications usable across congested networks and created a reusable foundation for interoperable 6G services and open developer access.

  • Customer Experience
  • Scalability
  • Speed to Market

Solution

Capgemini contributed key AI and edge-orchestration components to the 6G-XR project so holographic communication and distributed XR workloads could run across a multi-site European testbed. The solution combines a network congestion function with a predictive algorithm that anticipates traffic problems, then automatically reprioritizes data flows to keep immersive sessions usable under uplink pressure. In parallel, Capgemini helped build a demonstrator that selects the best nearby server in real time based on user location and network conditions, showing how cloud-edge migration can keep latency low for demanding XR experiences. The company also deployed its Intelligence Edge Application Platform (IEAP) at the Madrid node, exposed capabilities through open APIs, and helped connect Madrid and Barcelona using the GSMA Open Gateway model, turning AI-driven resource orchestration into an operational 6G building block rather than a lab concept.

Data flow

XR and holographic workloads enter the federated testbed, where the congestion function and predictive algorithm assess network conditions and adjust traffic handling. IEAP and the web-based controller then orchestrate application placement across edge nodes, selecting the best nearby server based on location and network status so low-latency experiences can be maintained.

Solution architecture

4 components · 3 layers
  1. Compute
    • predictive algorithm Anticipates congestion issues before they degrade XR communication quality.
  2. Serving
    • web-based controller Lets teams run end-to-end experiments across the federated testbed from a single interface.
  3. Orchestration
    • network congestion function Automatically adjusts traffic priorities when holographic workloads threaten to saturate the network.
    • Intelligence Edge Application Platform (IEAP) Orchestrates applications on nearby servers and exposes network functions through open APIs.

Architecture clues

  • Capgemini deployed IEAP at the Madrid node
  • Cloud-edge migration is treated as a core 6G enabler
  • Server selection responds dynamically to user location and network status
  • The project exposed capabilities through open APIs for external developers

Evidence from the source

"create a demonstrator that automatically selects the closest and most optimal server based on user location and network status"
"project partners created a network congestion function that activates a system to automatically adjust data traffic priorities."
"the 6G-XR project has developed a multi-site European testbed platform"
"we deployed our Intelligence Edge Application Platform (IEAP)"
"we developed a complimentary predictive algorithm to anticipate such issues."
Transparency Evidence & methodology 71/100 reference value · 91% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value71/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence91%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Capgemini

    A crucial step toward 6G infrastructure for immersive, interoperable, and human-centered communication - Capgemini

    First-party
    https://www.capgemini.com/news/client-stories/a-crucial-step-toward-6g-infrastructure-for-immersive-interoperable-and-human-centered-communication/
    Source type
    Vendor-published customer story
    Published
    Feb 23, 2026
    Supports
    • Customer
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Feb 23, 2026 · Capgemini · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.