The challenge of evaluating and modeling Quality of Experience (QoE) for content streaming video services is discussed in this paper. Based on the QoE evaluation methodology proposed by the EU-funded TRIANGLE project, a novel no-reference QoE model is presented to predict the user Mean Opinion Score (MOS) for videos streamed services such as YouTube. The study has been performed in a testbed that can control radio and network parameters, enabling real-life scenarios for mobile applications and devices in a controllable and repeatable manner. This work takes advantage of Amazon Mturk as a crowdsourcing platform to collect user opinions at scale, efficiently and quickly harvesting opinion scores from a much more diverse population than it would be possible from a local human panel. The performance of the proposed no-reference model is compared with PEVQ-S, a full-reference standardized algorithm used in the industry, showing better performance but with a much simpler approach. The performance has also been evaluated in SQoE-III, a publicly available adaptive video streaming database, to ensure the robustness of the model.

This paper addresses the challenges associated with modeling and evaluation of Quality of Experience (QoE) for content streaming video services. A novel full-reference QoE model is introduced, aiming to predict the user Mean Opinion Score (MOS) for videos streamed from any content streaming platform, although focusing on YouTube. Screen captures of streamed videos were used for an immersive user-experience investigation, avoiding reliance solely on packet analysis. FFmpeg, a widely-used open-source video processing software, and VMAF, an open-source video quality metric by Netflix, are the primary tools used by the proposed approach. The study is carried out in a testbed that allows full control of the radio and network parameters, enabling the emulation of realistic scenarios of mobile networks and establishing actual 5G connections with commercial mobile devices. To gather a diverse range of user opinions and conveniently scale up the dataset, Amazon MTurk is used as a crowdsourcing platform. Data from real users scoring 150 distorted video sequences have been used to train the machine learning algorithm based on QoE model developed in this work. The performance of the proposed model is compared with PEVQ-S, which is a standardized full reference algorithm showing better performance in a comprehensive set of performance metrics.

Over the past few years, the concept of Virtual Reality (VR) has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by the VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation, focusing on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode may have significant frame size fluctuations, impacting NS optimization. We then exploited the models to dynamically determine requirements in an NS scenario, providing the required QoS while minimizing resource usage.

The latest IEEE 802.11 amendments provide support to directional communications in the Millimeter Wave spectrum, thus making it possible to wirelessly approach several emerging use cases, such as eXtended Reality (XR), telepresence, and remote control of industrial facilities. However, these applications require stringent Quality of Service (QoS), that only contention-free scheduling algorithms can guarantee. In this paper, we propose a framework for the joint admission control and scheduling of periodic traffic streams over mmWave Wireless Local Area Networks based on Network Simulator 3 (ns-3), a popular full-stack open-source network simulator. Moreover, we design a baseline algorithm to handle scheduling requests, and evaluate its performance with a full-stack perspective. The algorithm is tested in three scenarios, where we investigated different configurations and features to highlight the trade-offs between contention-based and contention-free access strategies.

Over the past few years, eXtended Reality (XR) has attracted increasing interest thanks to its extensive industrial and commercial applications, and its popularity is expected to rise exponentially over the next decade. However, the stringent Quality of Service (QoS) constraints imposed by XR’s interactive nature require Network Slicing (NS) solutions to support its use over wireless connections: in this context, quasi-Constant Bit Rate (CBR) encoding is a promising solution, as it can increase the predictability of the stream, making the network resource allocation easier. However, traffic characterization of XR streams is still a largely unexplored subject, particularly with this encoding. In this work, we characterize XR streams from more than 4 hours of traces captured in a real setup, analyzing their temporal correlation and proposing two prediction models for future frame size. Our results show that even the state-of-the-art H.264 CBR mode can have significant frame size fluctuations, which can impact the NS optimization. Our proposed prediction models can be applied to different traces, and even to different contents, achieving very similar performance. We also show the trade-off between network resource efficiency and XR QoS in a simple NS use case.

Millimeter wave (mmWave) communication represents one of the main innovations of the next generation of wireless technologies, allowing users to reach unprecedented data rates. To overcome the high path loss at mmWave frequencies, these systems make use of directional antennas able to focus the transmit power into narrow beams using BeamForming (BF) techniques, thus making the communication directional. This new paradigm opens up a set of challenges for the design of efficient wireless systems, in which antenna and BF components play an important role also at the higher layer of the protocol stack. For this reason, accurate modeling of these components in a full-stack simulation is of primary importance to understand the overall system behavior.This paper proposes a novel framework for the end-to-end simulation of 5G mmWave cellular networks, including a raytracing based channel model and accurate models for antenna arrays and BF schemes. We showcase this framework by evaluating the performance of different antenna and BF configurations considering both link-level and end-to-end metrics and present the obtained results.

The millimeter wave (mmWave) band will provide multi-gigabits-per-second connectivity in the radio access of future wireless systems. The high propagation loss in this portion of the spectrum calls for the deployment of large antenna arrays to compensate for the loss through high directional gain, thus introducing the need for a spatial dimension in the channel model to accurately represent the performance of a mmWave network. In this perspective, ray tracing can characterize the channel in terms of Multi Path Components (MPCs) to provide a highly accurate model, at the price of extreme computational complexity (e.g., for processing detailed environment information about the propagation), which may limit the scalability of the simulations. In this paper, we present possible simplifications to improve the trade-off between accuracy and complexity in ray-tracing simulations at mmWaves by reducing the total number of MPCs. The effect of such simplifications is evaluated from a full-stack perspective through end-to-end simulations, testing different configuration parameters, propagation scenarios, and higher-layer protocol implementations. We then provide guidelines on the optimal degree of simplification, for which it is possible to reduce the complexity of simulations with a minimal reduction in accuracy for different deployment scenarios.

5G and beyond networks will use, for the first time ever, the millimeter wave (mmWave) spectrum for mobile communications. Accurate performance evaluation is fundamental for the design of reliable mmWave networks, with accuracy rooted in the fidelity of the channel models. At mmWaves, the model must account for the spatial characteristics of propagation since networks will employ highly directional antennas to counter the much greater pathloss. In this regard, Quasi-Deterministic (QD) models are highly accurate channel models, which characterize the propagation in terms of clusters of multipath components, given by a reflected ray and multiple diffuse components of any given Computer Aided Design (CAD) scenario. This paper introduces a detailed mathematical formulation for QD models at mmWaves, that can be used as a reference for their implementation and development. Moreover, it compares channel instances obtained with an open source National Institute of Standards and Technology (NIST) QD model implementation against real measurements at 60 GHz, substantiating the accuracy of the model. Results show that, when comparing the proposed model and deterministic rays alone with a measurement campaign, the Kolmogorov-Smirnov (KS) test of the QD model improves by up to 0.537.

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation- based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.

Structure-from-Motion (SfM) algorithms have recently been employed to reconstruct 3D scenes or environments from large sets of unordered images which were harvested from the web. Unfortunately, the accuracy of the reconstruction is significantly affected by the quality and the amount of editing operated on the processed images. Indeed, 3D modelling can significantly benefit from including forensic analysis strategies that are able to reconstruct the processing history of the processed images and select the most reliable pieces of visual information. The current paper presents an SfM strategy that orders the different views/images of the scene in the reconstruction process according to a processing age metric, i.e., a metric parameterizing the amount of processing stages operated on each image. Experimental results show that the proposed solution can improve the estimation accuracy of both 3D points and camera parameters with respect to state-of-the-art solutions.