The rapid evolution of telecommunication networks is leading to increasingly complex systems, requiring adaptive, flexible, and intelligent mechanisms for resource management, orchestration, and access control. In this context, the Network Digital Twin (NDT) paradigm emerges as a powerful tool to model the behavior of devices, communication links, operating environments, and applications in complex networks. This paper introduces FALCON, a Digital-Twin-based orchestration framework designed to optimize horizontal offloading in UAV-based Flying Ad Hoc Networks (FANETs) providing edge computing services to ground devices in remote areas. FALCON integrates multiple Smart Agents (DQN, A2C, PPO) running concurrently on the Digital Twin to dynamically determine the optimal offloading probabilities. A proof-of-concept demonstrates how the framework performs real-time What-if Scenario analyses and adapts to varying workload and channel conditions. Numerical results highlight the gains achieved through coordinated model selection and reuse, showing reduced end-to-end delay and faster convergence compared to standalone DRL-based controllers. • Digital Twin framework for real-time FANET orchestration. • Parallel Smart Agents enable fast What-if scenario evaluation. • Dynamic model selection adapts to FANET state and intents. • Reduced service delay compared to standalone DRL methods. • Model reuse ensures rapid reaction to changing UAV conditions.

Cloud gaming is a rapidly growing domain that requires low-latency and high-responsiveness architectures to deliver an engaging user experience. This paper presents an innovative Digital Twin (DT)-based framework designed to mitigate the effects of variable network conditions on video streaming and input responsiveness. The proposed architecture integrates Console and User Equipment DTs, which synchronize video streams and user inputs using lightweight RTSP and MQTTbased communication, ensuring real-time interaction even in challenging network scenarios. To validate the framework, we developed a custom cloud gaming application simulating a shark-and-fish pursuit game. This use case allows for precise evaluation of system performance under varying latency and delay conditions. Experimental results demonstrate the architecture’s ability to maintain game responsiveness, revealing critical delay thresholds and highlighting the impact of delay variability on user performance. These findings illustrate the potential of DTbased architectures to enhance the robustness and fairness of cloud gaming systems.

Virtual Reality (VR) and Augmented Reality (AR) are revolutionizing immersive 360° video experiences across education, entertainment, and cultural exploration. This paper introduces a Smart Video Encoder (SVE) system, comprising server- and user-side Virtual Network Functions (VNFs), for adaptive compression and transmission of 360° video streams over 5G/6G networks. Leveraging a Supervised Machine Learning (SML) Video Analyzer, the system enhances user-perceived quality by prioritizing key regions, such as artistic content. Using hierarchical tile-based compression, the SVE adjusts compression dynamically based on real-time user behavior and video content. Results show its effectiveness in maintaining high-quality experiences under bandwidth constraints, particularly for applications like virtual museum tours.

Virtual reality (VR) cloud gaming faces significant challenges in terms of latency and bandwidth consumption, particularly in softwarized 5 G and beyond (5G&B) networks. Ensuring an immersive experience on low-end devices, such as smartphones and cardboard-like headsets, requires innovative solutions to mitigate delay effects and optimize resource usage. This research focuses on enhancing VR cloud gaming performance by leveraging Digital Twins (DTs) and hierarchical compression techniques. The proposed system adopts a client-server architecture, where computationally intensive tasks are offloaded to the server, minimizing processing requirements on the client side. A key contribution is a novel delay estimation and management framework that employs DTs to synchronize video streaming and user input, reducing the impact of latency. Additionally, viewport-based hierarchical compression is applied to optimize bandwidth consumption while maintaining high perceived quality. The proposed architecture is evaluated through extensive experiments analyzing the effects of input delay, delay fluctuations, and compression methods on user performance and perceived quality. Results demonstrate that the system effectively mitigates adverse network conditions, ensuring a seamless and immersive VR gaming experience even in suboptimal environments.

The entertainment and gaming industries continue to evolve, so they are becoming increasingly reliant on advanced network capabilities to deliver immersive, real-time experiences. In-network computing (INC) is a transformative paradigm in the design of the network architecture in 6G networks. It facilitates the offloading of computational tasks from devices to edge nodes and central servers, enabling faster data processing in network nodes and reducing latency and response times for high-demand applications, enhancing efficiency. This way, it will be possible to provide the final users with advanced applications like Augmented Reality (AR) and Virtual Reality (VR). In this paper we introduce a 6G Network Infrastructure testbed designed for application execution and testing, supporting INC, and we present an Adaptive 360° Video Streaming service using INC facilities. The testbed has been designed to represent a vertical application for SLICES-RI and SUNRISE-6G projects. Some numerical results will assesses the performance of this dynamic video streaming system.

In the last few years, Virtual Reality (VR) is assuming a prominent role and is recognized as a pivotal technology in various sectors. However, transmission of immersive videos produced by real-time 360° cameras or stored on remote servers to reproduce 3D environments, or streamed by video games accessible through headsets, would require a lot of network bandwidth that, in many cases, is not available or too expensive to be obtained. In this paper, we leverage on network softwarization provided by new 5G&B networks, and introduce an Adaptive Closed-loop Encoding VNF named 360-ST for adaptive compression of 360° video streaming. This VNF is able to apply a hierarchical compression that takes into account both the bandwidth currently available in the network, and the user viewport. The agent that is in charge of deciding the different compression ratio at runtime uses Deep Reinforcement Learning to optimize a reward function and adapt to the changes of the network bandwidth, the end-to-end latency, the user movements within the scene and the video content. The results indicate that our proposed method consistently outperforms state-of-the-art algorithms by an average of 8% to 46% in terms of achieved Peak Signal-to-Noise Ratio (PSNR).