Virtual Network Functions (VNFs) are essential components in modern networking that decouple network functions from dedicated hardware, thereby enhancing flexibility, scalability, and cost-efficiency. The inherently dynamic nature and fluctuating loads of networks, combined with evolving network demands, often lead to the over-allocation or underprovisioning of VNF resources, posing a significant challenge in optimal VNF resource usage. This issue becomes even more challenging when VNFs are connected in a specific sequence to fulfill a functional need-Service Function Chain (SFC). A promising solution to this challenge is proactive resource management-predicting resource demands of VNFs in advance. In this paper, we present a performance metrics prediction framework designed to anticipate multi-step resource demands for VNFs. The framework employs machine learning models, including RNN, LSTM, GRU, and Transformer models, within a robust meta-learner to capture complex usage patterns, accounting for both short-term and long-term dependencies in VNF resource consumption. This approach allows for precise prediction of critical key performance indicators (KPIs), including CPU usage, memory usage, processing latency, and traffic load for each VNF within an SFC. The evaluation results show that the proposed framework achieves a 75% reduction in mean absolute error (MAE) compared to the Transformer model and over 84% compared to RNN, LSTM, and GRU models. These results demonstrate the framework’s substantial improvement over state-of-the-art approaches.

Multi-Access Edge Computing (MEC) is a promising solution for providing the computational resources and low latency required by vehicular services, such as autonomous driving. It enables cars to offload computationally intensive tasks to nearby servers. Effective offloading involves determining when to offload tasks, selecting the appropriate MEC site, and efficiently allocating resources to ensure optimal performance. While car mobility poses significant challenges to guaranteeing reliable task completion, today we lack energy-efficient solutions to solve this problem, especially when considering real-world car mobility traces. In this paper, we begin by examining the mobility patterns of cars using data obtained from a leading mobile network operator in Europe. Based on the insights from this analysis, we design an optimization problem for task computation/offloading, considering both static and mobility scenarios. Our objective is to minimize the total energy consumption—both at the cars and the MEC nodes—while satisfying the latency requirements of various tasks. We evaluate our solution, based on multi-agent reinforcement learning, both in simulations as well as in a realistic setup that relies on datasets from the operator. Our solution shows a significant reduction of user dissatisfaction and task interruptions in both static and mobile scenarios, while achieving energy savings of 47% (static) and 14% (mobile) compared to state-of-the-art schemes.

Haptic teleoperation systems mark a critical breakthrough in remote manipulation technologies, delivering immersive user experiences through precise control and tactile feedback. This paper aims to explore the interplay between Quality of Service (QoS) network metrics, such as latency, jitter, and packet loss, and Quality of Experience (QoE) features, including immersion, control, and engagement. This investigation is conducted through a combination of objective measurements and subjective evaluations over a private Fifth Generation Standalone (5G SA) network. In addition, it aims to realize the extent to which transport-layer protocols operating over IP, such as TCP and UDP, as well as environmental factors like indoor and outdoor settings, influence system performance.Results confirm the well-established trade-off between reliability and latency in transport protocols, with TCP offering higher reliability in controlled indoor environments, and UDP exhibiting better responsiveness in dynamic outdoor scenarios. While these findings align with existing knowledge, their empirical validation in the context of immersive haptic applications over a private 5G SA network reinforces their relevance. In addition, QoE metrics were found to be linked to QoS indicators, highlighting the importance of balancing speed, stability, and reliability. These findings provide valuable insights towards designing adaptive teleoperation systems capable of dynamically optimizing performance under diverse conditions.

After a rapid deployment worldwide over the past few years, 5G is expected to have reached a mature deployment stage to provide measurable improvement of network performance and user experience over its predecessors. In this study, we aim to assess 5G deployment maturity via three conditions: (1) Does 5G performance remain stable over a long time span (1 year)? (2) Does 5G provide better performance than its predecessor Long-Term Evolution (LTE)? (3) Does the technology offer similar performance across diverse geographic areas and cellular operators? We answer this important question by conducting two year-long measurement campaigns of 5G uplink performance leveraging a custom Android app: one crowd-sourced, cross-sectional campaign spanning 8 major cities in 7 countries and two different continents (Europe and North America), and one controlled campaign focusing on mmWave deployment at a fixed location in the downtown area of Boston, MA. Our datasets show that 5G deployment in major cities appears to have matured, with no major performance improvements observed over a one-year period, but 5G does not provide consistent, superior measurable performance over LTE, especially in terms of latency, and further there exists clear uneven 5G performance across the 8 cities. Our study suggests that, while 5G deployment appears to have stagnated, it is short of delivering its promised performance and user experience gain over its predecessor.

The Narrowband Internet of Things (NB-IoT) technology has an important role in the mobile cellular ecosystem, enabling massive Machine Type Communication (mMTC) services. NB-IoT propagation was preliminarily analyzed via a measurement campaign carried out in 2020 in the city of Oslo, Norway. This investigation resulted in Oslo-2020, the first NB-IoT-specific Alpha-Beta-Gamma (ABG) path loss (PL) model, which showed higher prediction accuracy compared to models developed for different technologies but often used for NB-IoT. In this paper, to further investigate NB-IoT PL in urban scenarios, we analyze new measurement campaigns performed in 2020-2021 and 2023 in the city of Rome, Italy. First, we use the 2020-2021 measurements to derive Rome-2021, a new NB-IoT-specific ABG PL model. We show that Rome-2021 preserves the statistical properties of Oslo-2020 (e.g., the Gaussianity of the PL exponent distribution across base stations), although the moments of the distributions are different due to city-specific environmental characteristics. We also use new data on signal losses due to outdoor-to-indoor propagation to refine the analysis of this scenario. Finally, we propose a methodology to combine Oslo-2020 and Rome-2021 into a more general model. Our methodology uses so-called Mixture Distributions (MDs), thus leveraging the shared statistical properties between Oslo-2020 and Rome-2021. By using the 2023 measurements, we show that our MD-based approach estimates PL model parameters with higher accuracy compared to Oslo-2020 and Rome-2021 models used separately, thus providing an effective solution for predicting NB-IoT urban PL in lack of site-specific measurements and information.

Mobile Augmented Reality (MAR) applications pose unique challenges due to computation intensity, constrained device resources, and high interactive rendering requirements. The emergence of 5 G and edge computing offers opportunities to offload computation to the edge and cloud, indirectly enhancing the computing capability and usage duration of MAR devices. However, existing general task offloading and multipath transmission techniques do not address the challenges in offloading path selection with multiple edges, dynamic resource competition awareness, and spatial computation with strong task dependencies. This paper contributes FPSelector, a flexible path selector for MAR offloading. We present a two-tier MAR-specific offloading scheme with multiple edge nodes. In offloading decisions, we design a reinforcement learning model to generate the selection policy for each packet of an AR data stream. This model incorporates an action masking mechanism, a comprehensive reward function, and state features complemented by a resource prediction module, making FPSelector aware of dynamic heterogeneous environments. Moreover, we propose an online learning strategy to facilitate real-time selection. To validate its efficacy, we compare FPSelector's performance against leading schedulers under various scenarios, demonstrating a notable reduction of 9.9% and 9.6% in overall completion time for 4 K and 8 K video-based MAR applications compared to its closest competitor.

The mobile telecommunication industry is undergoing continuous evolution to cope with ever increasing service requirements and expectations of end users. This has recently led to the rise of Mobile Network Aggregators (MNAs), a new type of global virtual operators that deliver mobile communication services by utilizing multiple Mobile Network Operators (MNOs), dynamically connecting to the one that best meets their customers’ needs based on location and time. MNAs can then offer optimized global coverage by connecting to local MNOs that have limited (e.g., national) geographic service. In this paper, we provide a first in-depth analysis of the operations of three major MNAs: Google Fi, Twilio, and Truphone. We conduct performance measurements across these MNAs for critical applications spanning DNS, web browsing, and video streaming, and compare their performance against that of a traditional MNO from two very diverse geographical locations, US and Spain. We find that MNAs may introduce some delay compared to local MNOs in the region where the user is roaming, yet they offer significant performance improvements over the traditional MNOs roaming model, such as home-routed roaming. To fully assess the potential benefits of the MNA model, we also carry out emulation studies assessing the potential performance gains that MNAs could achieve by deploying both control and user plane functions of open-source 5G implementations across different Amazon Web Services locations.

The promises of multipath transport are to aggregate bandwidth, improve resource utilisation and enhance reliability. In this article, we demonstrate that the way multipath coupled congestion control is defined today leads to a suboptimal resource utilisation when network paths are disjoint, i.e., they do not share a bottleneck link. With growing interest in standardising Multipath QUIC (MPQUIC), we have implemented the practical Shared Bottleneck Detection (SBD) algorithm from RFC8382 in MPQUIC (MPQUIC-SBD). Through extensive experiments, we evaluate MPQUIC-SBD in the context of video streaming with various Adaptive Bitrate (ABR) algorithms, addressing both ABR classes of rule- and learning-based solutions. We demonstrate that MPQUIC-SBD accurately detects shared bottlenecks over 90% of the time, depending on the ABR algorithm, as the size of the video segments increases. In non-shared bottleneck scenarios, when MPQUIC-SBD detects that its QUIC subflows do not share the same network resources, it decouples their congestion windows accordingly, enabling video throughput gains of up to 37% compared to MPQUIC. These gains translate directly into improved video quality metrics, including higher bitrate, better resolution and reduced buffering, resulting in an enhanced quality of experience for users.

In this paper, we investigate the effects of asynchrony between the visual and haptic feedback in virtual reality (VR) on user experience, specifically focusing on understanding users' awareness of this asynchrony and its effect on their level of satisfaction. Using Unreal Engine, we created an experimental setup to adjust the timing between these sensory inputs. Our experiment featured a VR dodge game that provides haptic feedback on the body when the player is hit by a multitude of virtual objects. Conducting a targeted, small-scale user study, we aim to understand in what ways an introduced asynchrony influences the VR experience. The results highlight the perceptibility of asynchrony, which significantly affects the overall user experience. Nonetheless, we also find an asymmetry that benefits scenarios where haptic feedback precedes visual cues. Furthermore, our findings suggest that users can generally accept minor levels of asynchrony without significant disadvantages to their satisfaction. However, it is interesting to note that even when users cannot explicitly identify any asynchrony, they might still experience a slight decrease in satisfaction.

Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models.

The growing connected car market requires mobile network operators (MNOs) to rethink their network architecture to deliver ultra-reliable low-latency communications. In response, Multi-Access Edge Computing (MEC) has emerged as a solution, enabling the deployment of computing resources at the network edge. For MNOs to tap into the potential benefits of MEC, they need to transform their networks accordingly. Consequently, the primary objective of this study is to design a realistic MEC architecture and corresponding optimal deployment strategy – deciding on the placement and configuration of computing resources – as opposed to prior studies focusing on MEC run-time management and orchestration (e.g., service placement, computation offloading, and user allocation). To cater to the heterogeneous demands of vehicular services, we propose a multi-tier MEC architecture aligned with 5G and Beyond-5G radio access network deployments. Therefore, we frame MEC deployment as an optimization problem within this architecture, assuming 3 MEC tiers. Our data-driven evaluation, grounded in realistic assumptions about network architecture, usage, latency, and cost models, relies on datasets from a major MNO in the UK. We show the benefits of adopting a 3-tier MEC architecture over single-tier (centralized or distributed) architectures for heterogeneous vehicular services, in terms of deployment cost, energy consumption, and robustness.

Fifth Generation (5G) has been acknowledged as a significant shift in cellular networks, expected to run significantly different classes of services and do so with outstanding performance in terms of low latency, high capacity, and extreme reliability. Managing the resulting complexity of mobile network architectures will depend on making efficient decisions at all network levels based on end-user requirements. However, to achieve this, it is critical to first understand the current mobile ecosystem and capture the device heterogeneity, which is one of the major challenges for ensuring the successful exploitation of 5G technologies.In this paper, we conduct a large-scale measurement study of a commercial mobile operator in the UK, focusing on bringing forward a real-world view on the available network resources, as well as how more than 30M end-user devices utilize the mobile network. We focus on the current status of the 5G Non-Standalone (NSA) deployment and the network-level performance and show how it caters to the prominent use cases that 5G promises to support. Finally, we demonstrate that a fine-granular set of requirements is, in fact, necessary to orchestrate the service to the diverse groups of 5G devices, some of which operate in permanent roaming.

The promises of multipath transport is to aggregate bandwidth and improve resource utilisation and reliability. We demonstrate in this paper that the way multipath coupled congestion control is defined today RFC6359 leads to a sub-optimal resource utilisation when network paths are mainly disjoint, i.e., they do not share a bottleneck. With growing interest to standardise Multipath QUIC (MPQUIC), we implement the practical shared bottleneck detection (SBD) algorithm from RFC8382 in MPQUIC, namely MPQUIC-SBD. We evaluate MPQUIC-SBD through extensive emulation experiments in the context of video streaming. We show that MPQUIC-SBD is able to correctly detect shared bottlenecks over 90% of the time as the video segments' size increase depending on the Adaptive Bitrate (ABR) algorithm. In non-shared bottleneck scenarios, MPQUIC-SBD results in video throughput gains of more than 13% compared to MPQUIC, which directly translates into better video quality metrics.

5G mobile networks introduce the concept of network slicing, the functionality of creating virtual networks on top of shared physical infrastructure. Such slices can be tailored to various vertical services. A single User Equipment (UE) may be served by multiple network slice instances simultaneously, which opens up the possibility of dynamically steering traffic in response to the specific needs of individual applications – and as a reaction to events inside the network, e.g., network failures.This paper presents the PoLicy-based Architecture for Network Slicing (PLANS). In this policy framework, the network slice management entity in the 5G core and the UE can cooperatively optimize the usage of the available network slices via policy systems installed both inside the network and on the UE. The PLANS architecture has been implemented and evaluated in a 5G testbed. For two different case studies, we show how such a system can be leveraged to provide optimized services and increased robustness against network failures. First, we consider a drone autopilot scenario, and demonstrate how PLANS can reduce network-slice recovery time by more than 90%. Second, we illustrate for a 360°video streaming scenario how PLANS can help prevent video quality degradation when a network slice becomes unavailable.

A new model of global virtual Mobile Network Operator (MNO) - the Mobile Network Aggregator (MNA) - has recently been gaining significant traction. MNAs provide mobile communications services to their customers by leveraging multiple MNOs, and connecting through the one that best match their customers' needs at any point in time (and space). MNAs naturally provide optimized global coverage by connecting through local MNOs across the different geographic regions they provide service. In this paper, we dissect the operations of three MNAs, namely, Google Fi, Twilio and Truphone. We perform measurements using the three selected MNAs to assess their performance for three major applications, namely, DNS, web browsing and video streaming. We benchmark their performance comparing it to the one of a traditional MNO. We find that even MNAs provide some delay penalty compared to the service accessed through the local MNOs in the geographic area where the user is roaming, they can significantly improve performance compared to traditional roaming model of the MNOs (e.g. home routed roaming). Finally, in order to fully quantify the potential benefits that can be realized using the MNA model, we perform a set of emulations by deploying both control and user plane functions of open-source 5G implementations in different locations of AWS, and measure the potential gains.

“Roam like Home” is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people can use data services more freely across Europe. However, the implications of roaming solutions on network performance have not been carefully examined yet. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 4G networks in Europe, including mobile network configuration, performance characteristics, and quality of experience. We find that operators adopt a common approach to implement roaming called Home-routed roaming (HR). This results in additional latency penalties of 60 ms or more, depending on geographical distance. This leads to worse browsing performance, with an increase in the metrics related to Quality of Experience (QoE) of users (Page Load time and Speed Index) in the order of 15-20 percent. We further analyze in isolation the impact of latency on QoE metrics and find that the penalty imposed by HR leads to a degradation on QoE metrics up to 150 percent in case of intercontinental roaming.

Reliable consistent communication over millimeter-wave (mmWave) channels is a challenging problem due to their sensitivity to blocking of Line of Sight connections. MmWave is a key building block in 5G and future generation cellular networks, making solutions to this problem space important. Our aim is to use predictive control to manage and simultaneously use multiple available mmWave paths to achieve reliable consistent communication (i.e., steady transmission rate with low delay) with a multipath proxy. To this end we investigate transient solutions of Markov Modulated Fluid Queue models (MMFQ), apt because the mmWave blocking has been modeled with Markovian models. We propose a combination of models that can be solved using newly proposed matrix analytic techniques in a timely enough manner for use in real-time control. This gives us a prediction of either proxy queue distributions or probabilities of reaching proxy buffer levels over a short time horizon, enabling the proxy to make preemptive path decisions to maintain a desired Quality of Service. A proof of concept simulation study demonstrates the efficacy of our proposed MMFQ-based predictive approach over either static or purely reactive control approaches.

The lack of publicly available large-scale measurements has hindered the derivation of empirical path-loss (PL) models for Narrowband Internet of Things (NB-IoT). Therefore, simulation-based investigations currently rely on models conceived for other cellular technologies, which are characterized, however, by different available bandwidth, carrier frequency, and infrastructure deployment, among others. In this article, we take advantage of data from a large-scale measurement campaign in the city of Oslo, Norway, to provide the first empirical characterization of NB-IoT PL in an urban scenario. For the PL average term, we characterize Alpha-Beta-Gamma (ABG) and Close-In (CI) models. By analyzing multiple NB-IoT cells, we propose a statistical PL characterization, i.e., the model parameters are not set to a single constant value across cells, but are randomly extracted from well-known distributions. Similarly, we define the PL shadowing distribution, correlation over distance, and intersite correlation. Finally, we give initial insights on the outdoor-to-indoor propagation, using measurements up to deep indoor scenarios. The proposed models improve the PL estimation accuracy compared to the ones currently adopted in NB-IoT investigations, enabling more realistic simulations of urban scenarios similar to the sites covered by our measurements.

In the context of massive machine-type communications (mMTCs), the narrowband Internet-of-Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored random access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This article presents the first data-driven analysis of NB-IoT RA, exploiting a large-scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators' configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. A comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a machine learning (ML) approach and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in an RA-enhanced scheme and show that optimized configurations enable power consumption reduction of at least 50%. We also make our data set available for further exploration, toward the discovery of new insights and research perspectives.

Multipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths with dynamic path characteristics (i.e., packet loss, fluctuation of delay). In this paper, we propose Peekaboo, a novel learning-based multipath scheduler that is aware of the dynamic characteristics of the heterogeneous paths. Peekaboo is able to learn scheduling decisions to adopt over time based on the current path characteristics and dynamicity levels - from both deterministic and stochastic perspectives. We implement Peekaboo in Multipath QUIC (MPQUIC) and compare it with state-of-the-art multipath schedulers for a wide range of dynamic heterogeneous environments, upon both emulated and real networks. Our results show that Peekaboo outperforms the other schedulers by up to 31.2% in emulated networks and up to 36.3% in real network scenarios.

QUIC, fostered by Google and under standardization in the IETF, integrates some of HTTP/s, TLS, and TCP functionalities over UDP. One of its main goals is to facilitate transport protocol design, with fast evolution and innovation. However, congestion control in QUIC is still severely jeopardized by packet losses, despite implemented loss recovery mechanisms, whose behavior strongly depends on the Round Trip Time. In this paper, we design and implement rQUIC, a framework that enables FEC within QUIC protocol to improve its performance over wireless networks. The main idea behind rQUIC is to reduce QUIC's loss recovery time by making it robust to erasures over wireless networks, as compared to traditional transport protocol loss detection and recovery mechanisms. We evaluate the performance of our solution by means of extensive simulations over different type of wireless networks and for different applications. For LTE and Wifi networks, our results illustrate significant gains of up to 60% and 25% savings in the completion time for bulk transfer and web browsing, respectively.

Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the acsMONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.

Quick UDP Internet Connections (QUIC) is an experimental transport protocol designed to primarily reduce connection establishment and transport latency, as well as to improve security standards with default end-to-end encryption in HTTPbased applications. QUIC is a multiplexed and secure transport protocol fostered by Google and its design emerged from the urgent need of innovation in the transport layer, mainly due to difficulties extending TCP and deploying new protocols. While still under standardisation, a non-negligble fraction of the Internet's traffic, more than 7% of a European Tier1-ISP, is already running over QUIC and it constitutes more than 30% of Google's egress traffic [1].

In recent years, Downlink (DL) throughput estimation in Mobile Broadband (MBB) networks has gained immense popularity and it is expected to become a vital component of the upcoming fifth generation (5G) systems. Plentiful adaptive video streaming algorithms greatly rely on accurate DL throughput predictions to adapt their mechanisms and ensure high Quality of Service (QoS) to the end-users. Thus far, conventional DL throughput estimation approaches, also known as speed tests, require an extensive exchange of TCP traffic over the network for an allocated time duration. While such tools appear to deliver trustworthy results, they turn out to be inefficient when mobile subscriptions with limited data plans are engaged. In this paper, we propose a supervised Machine Learning (ML) solution for DL throughput estimation that aims at delivering highly accurate predictions while significantly limiting the over-the-air data consumption. We capture the network performance metrics by exploring both crowdsourced and controlled testing methodologies. We leverage RTR-NetTest, a platform of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), and MONROE-NetTest, its counterpart wrapper built as an Experiment as a Service (EaaS) on top of Measuring Mobile Broadband Networks in Europe (MONROE). Results reveal that our solution can achieve a 39.7% reduction in terms of data consumption while delivering a Median Absolute Percentage Error (MdAPE) of 5.55%. We further show that accuracy can be traded-off, for example, a significant data consumption reduction of 95.15% can be achieved for a MdAPE of 20%.

Measuring and characterizing web page performance is a challenging task. When it comes to the mobile world, the highly varying technology characteristics coupled with the opaque network configuration make it even more difficult. Aiming at reproducibility, we present a large scale empirical study of web page performance collected in eleven commercial mobile networks spanning four countries. By digging into measurement from nearly two million web browsing sessions, we shed light on the impact of different web protocols, browsers, and mobile technologies on the web performance. We find that the impact of mobile broadband access is sizeable. For example, the median page load time using mobile broadband increases by a third compared to wired access. Mobility clearly stresses the system, with handover causing the most evident performance penalties. Contrariwise, our measurements show that the adoption of HTTP/2 and QUIC has practically negligible impact. To understand the intertwining of all parameters, we adopt state-of-the-art statistical methods to identify the significance of different factors on the web performance. Our analysis confirms the importance of access technology and mobility context as well as webpage composition and browser. Our work highlights the importance of large-scale measurements. Even with our controlled setup, the complexity of the mobile web ecosystem is challenging to untangle. For this, we are releasing the dataset as open data for validation and further research.

The demand for mobile communication is continuously increasing, and mobile devices are now the communication device of choice for many people. To guarantee connectivity and performance, mobile devices are typically equipped with multiple interfaces. To this end, exploiting multiple available interfaces is also a crucial aspect of the upcoming 5G standard for reducing costs, easing network management, and providing a good user experience. Multi-path protocols, such as multi-path TCP (MPTCP), can be used to provide performance optimization through load-balancing and resilience to coverage drops and link failures, however, they do not automatically guarantee better performance. For instance, low-latency communication has been proven hard to achieve when a device has network interfaces with asymmetric capacity and delay (e.g., LTE and WLAN). For multi-path communication, the data scheduler is vital to provide low latency, since it decides over which network interface to send individual data segments. In this paper, we focus on the MPTCP scheduler with the goal of providing a good user experience for latency-sensitive applications when interface quality is asymmetric. After an initial assessment of existing scheduling algorithms, we present two novel scheduling techniques: the block estimation (BLEST) scheduler and the shortest transmission time first (STTF) scheduler. BLEST and STTF are compared with existing schedulers in both emulated and real-world environments and are shown to reduce web object transmission times with up to 51% and provide 45% faster communication for interactive applications, compared with MPTCP's default scheduler.

Hyperparameter optimization is an important but often ignored part of successfully training Neural Networks (NN) since it is time consuming and rather complex. In this paper, we present HINDSIGHT, an open-source framework for designing and implementing NN that supports hyperparameter optimization. HINDSIGHT is built entirely in R and the current version focuses on Long Short Term Memory (LSTM) networks, a special kind of Recurrent Neural Networks (RNN). HINDSIGHT is designed in a way that it can easily be expanded to other types of Deep Learning (DL) algorithms such as Convolutional Neural Networks (CNN) or feed-forward Deep Neural Networks (DNN). The main goal of HINDSIGHT is to provide a simple and quick interface to get started with LSTM networks and hyperparameter optimization.

Video streaming is a very popular service among the end-users of Mobile Broadband (MBB) networks. DASH and WebRTC are two key technologies in the delivery of mobile video. In this work, we empirically assess the performance of video streaming with DASH and WebRTC in operational MBB networks, by using a large number of programmable network probes spread over several countries in the context of the MONROE project. We collect a large dataset from more than 300 video streaming experiments. Our dataset consists of network traces, performance indicators captured during the streaming sessions, and experiment metadata. The dataset captures the wide variability in video streaming performance, and unveils how mobile broadband is still not offering consistent quality guarantees across different countries and networks, especially for users on the move. We open source our complete software toolset and provide the video dataset as open data.

"Roam like Home" is the initiative of the European Commission (EC) to end the levy of extra charges when roaming within the European region. As a result, people are able to use data services more freely across Europe. However, the implications roaming solutions have on performance have not been carefully examined. This paper provides an in-depth characterization of the implications of international data roaming within Europe. We build a unique roaming measurement platform using 16 different mobile networks deployed in six countries across Europe. Using this platform, we measure different aspects of international roaming in 3G and 4G networks, including mobile network configuration, performance characteristics, and content discrimination. We find that operators adopt common approaches to implementing roaming, resulting in additional latency penalties of ∼60 ms or more, depending on geographical distance. Considering content accessibility, roaming poses additional constraints that leads to only minimal deviations when accessing content in the original country. However, geographical restrictions in the visited country make the picture more complicated and less intuitive.

The accuracy of measurement-driven mobile coverage maps depends on the quality, density and pattern of the signal strength observations. Thus, identifying an efficient measurement data collection methodology is essential, especially when considering the cost associated with the measurement collection approaches (e.g., drive tests, crowd approaches). We propose ZipWeave, a novel measurement data collection and fusion framework for building efficient and reliable measurement-based mobile coverage maps. ZipWeave incorporates a novel nonuniform sampling strategy to achieve reliable coverage maps with reduced sample size. Assuming prior knowledge of the propagation characteristics of the region of interest, we first examine the potential gains of this non-uniform sampling strategy in different cases via a measurement-based statistical analysis methodology; this involves irregular spatial tessellation of the region of interest into sub-regions with internally similar radio propagation characteristics and sampling based on these sub-regions. We then present a practical form of ZipWeave nonuniform sampling strategy that can be used even without any prior information. In all our evaluations, we show that the ZipWeave non-uniform sampling approach reduces the samples by half compared to the common systematic-random sampling, while maintaining similar accuracy. Moreover, we show that the other key feature of ZipWeave to combine high-quality controlled measurements (that present limited geographic footprint similar to drive tests) with crowdsourced measurements (that cover a wider footprint) leads to more reliable mobile coverage maps overall.

Open experimentation with operational Mobile Broadband (MBB) networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need of innovative solutions for mobile communications. Even more, there is a strong need for objective data about stability and performance of MBB (e.g., 3G/4G) networks, and for tools that rigorously and scientifically assess their status. In this paper, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements and custom experimentation on operational MBB networks. The MONROE platform enables accurate, realistic and meaningful assessment of the performance and reliability of 11 MBB networks in Europe. We report on our experience designing, implementing and testing the solution we propose for the platform. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. We describe and exemplify the capabilities of the platform and the wide variety of experiments that external users already perform using the system.

Crowdsourcing mobile network performance evaluation is rapidly gaining popularity, with new applications aiming to deliver more accurate and reliable results every day. From the perspective of end-users, these utilities help them estimate the performance of their service provider in terms of throughput, latency and other key performance indicators of the network. In this paper, we build ORCA: Operator Classifier, a Machine Learning (ML) based framework to define and determine the behavior of Mobile Network Operators (MNOs) from crowdsourced datasets. We investigate whether one can differentiate MNOs by using crowdsourced end-to-end network measurements. We consider different performance metrics (e.g. Download (DL)/Upload (UL) data rate, latency, signal strength) and study the impact of them individually but also collectively on differentiating MNOs. We use RTR Open Data, an open dataset of broadband measurements provided by the Austrian Regulatory Authority for Broadcasting and Telecommunications (RTR), to characterize the three major mobile native operators and two virtual operators in Austria. Our results show that ORCA can be used to identify patterns between various mobile systems and disclose their differences from the end-user perspective.

One of the main goals of multipath TCP (MPTCP) is to achieve higher throughput than regular TCP by utilizing multiple paths simultaneously. When these paths share a common bottleneck, MPTCP tries not to be more aggressive than a regular TCP flow. This is achieved by MPTCP's coupled congestion control mechanism that couples the increase factor of MPTCP's subflows in congestion avoidance. However, slow-start remains unchanged and behaves uncoupled for each subflow, affecting MPTCP and concurrent traffic at the bottleneck. We propose LISA, a simple algorithm for coupling MPTCP subflows in slow-start, and investigate the trade-off that this coupling entails. Our evaluations show that coupling in slow-start not only provides gains for MPTCP but also for a concurrent TCP at the bottleneck.

Multipath TCP (MPTCP) enables the simultaneous use of multiple links for bandwidth aggregation, better resource utilization and improved reliability. Its coupled congestion control intends to reap the increased bandwidth of multiple links, while avoiding being more aggressive than regular TCP flows on every used link. We argue that this leads to a very conservative behavior when paths do not share a bottleneck. Therefore, in this paper, we first quantify the penalty of the coupled congestion control for links that do not share a bottleneck. Then, in order to overcome this penalty, we design and implement a practical shared bottleneck detection (SBD) algorithm for MPTCP, namely MPTCP-SBD. Through extensive emulations, we show that MPTCP-SBD outperforms all currently deployed MPTCP coupled congestion controls by accurately detecting bottlenecks. For the non-shared bottleneck scenario, we observe throughput gains of up to 40% with two subflows and the gains increase significantly as the number of subflows increase, reaching more than 100% for five subflows. Furthermore, for the shared bottleneck scenario, we show that MPTCP-SBD remains fair to TCP. We complement the emulation results with real-network experiments justifying its safeness for deployment.

In this paper, we present a cooperative video multicast protocol for densely deployed small cells. We assume that the small cell Base Stations (BS) have overlapping coverages and they are allowed to have interfering transmissions. The proposed cooperative multicast protocol realizes a two-hop transmission system where each hop has its own time slot. In the first hop, the small cell base stations simultaneously transmit the packets to the relays who are also the users of multicast transmission. The relays then follow the optimal strategy under interference conditions and apply successive interference cancellation (SIC) decoding. In the second hop, the relays apply a Distributed Space Time Code (DSTC) that dynamically adapts itself based on the result of SIC and broadcast the result. This cooperative transmission scheme is complemented with application layer Forward Error Correction (FEC) in order to handle the remaining packet level losses. Through extensive simulations, we investigate the performance of the proposed scheme and show that the proposed protocol can efficiently handle inter-user interference, leading to superior performance over the state-of-the-art purely orthogonal cooperative transmission schemes.

In this paper, we investigate low-complexity, receiver-driven strategies for the transmission of two correlated sources in a multiple access quasi-static fading channel. We consider two different transmission modes at the physical (PHY) layer depending on the average signal to noise ratio (SNR) of the involved Rayleigh fading channels. The two modes that are allowed are orthogonal transmissions and interfering transmissions with successive interference cancellation (SIC) decoding. Further, we consider the sources are compressed independently while they are jointly decoded with an adaptive linear minimum mean square error (MMSE) algorithm. Hence, the receiver selects the combination of a PHY transmission mode that should be used at the sources, together with the adaptive MMSE-based estimation of each source. We show that in different SNR regimes, a different transmission strategy is optimum, that is in the low SNR regime interfering transmission together with MMSE decoding is better, while in the high SNR regime orthogonal transmission is superior. Both schemes outperform distributed source coding (DSC) based approaches regardless of the degree of the correlation of the two sources.

Mobile broadband (MBB) networks underpin numerous vital operations of the society and are arguably becoming the most important piece of the communications infrastructure. In this demo paper, our goal is to showcase the potential of a novel multi-homed MBB platform for measuring, monitoring and assessing the performance of MBB services in an objective manner. Our platform, MONROE, is composed of hundreds of nodes scattered over four European countries and a backend system that collects the measurement results. Through a user-friendly web client, the experimenters can schedule and deploy their experiments. The platform further embeds traffic analysis tools for real-time traffic flow analysis and a powerful visualization tool.

In this paper, we consider Distributed Estimation (DES) in a Wireless Sensor Network (WSN) and assume that the number of sensors in the WSN is larger than the available number of transmission slots. With classic DES, the sensors independently transmit the sampled digitized data. However, the WSN is an uplink multiuser channel where multiple sources share the channel for communicating data to a Fusion Center (FC). To this aim, we adopt the optimal communication scheme for this setup that suggests interfering transmissions and the use of Successive Interference Cancelation (SIC) at the FC. We propose a joint SIC decoder and linear Minimum-Mean-Square-Error (MMSE) estimator for digital interfering transmission of correlated data. We further introduce an optimization framework that schedules and allocates power to the sensors optimally. We formulate the problem in two ways: an expected distortion minimization problem under a total power budget, and a transmission power minimization problem under a distortion constraint. For both cases, we consider the system performance under different operating conditions, and we demonstrate the efficiency of the proposed scheme compared to a system that employs optimized sensor selection under orthogonal transmissions.

Multi-path transfer protocols such as Concurrent Multi-Path Transfer for SCTP and Multi-Path TCP (MPTCP), are becoming increasingly popular, due to widespread deployment of smartphones with multi-homing support. Although the idea of using multiple interfaces simultaneously to improve application throughput is tempting, does transmission over multiple interfaces always provide benefits especially in realistic setup? In this paper, we first show that multi-path transfer might actually have a negative impact in real-world scenarios with mobile broadband and wireless LAN networks. We then introduce our Dynamic Relative Path Scoring (DRePaS) algorithm that continuously evaluates the contribution of paths to the overall performance and dynamically influences the scheduling decisions to make best use of the paths for the overall system performance. We show that DRePaS outperforms the current MPTCP implementation in terms of throughput and application delay, especially when the links are heterogeneous.

Today, most of the smart phones are equipped with two network interfaces: Mobile Broadband (MBB) and Wireless Local Area Network (WLAN). Multi-path transport protocols provide increased throughput or reliability, by utilizing these interfaces simultaneously. However, multi-path transmission over networks with very different QoS characteristics is a challenge. In this paper, we studied Multi-Path TCP (MPTCP) in heterogeneous networks, specifically MBB networks and WLAN. We first investigate the effect of bufferbloat in MBB on MPTCP performance. Then, we propose a bufferbloat mitigation algorithm: Multi-Path Transport Bufferbloat Mitigation (MPT-BM). Using our algorithm, we conduct experiments in real operational networks. The experimental results show that MPT-BM outperforms the current MPTCP implementation by increasing the application goodput quality and decreasing MPTCP's buffer delay, jitter and buffer space requirements.

Interfering transmissions of independent sources is shown to provide benefits at a cost of complex receiver design. In this paper, we study the optimum transmission of correlated Gaussian sources by allowing opportunistic interference in order to minimize the expected distortion. We assume a successive interference cancellation decoder at the destination and consider a Wyner-Ziv type setup to transmit the correlated sources along with interference. We show how interference can be utilized to improve the end-to-end distortion for correlated sources.

Two-way real-time video communication in wireless networks requires high bandwidth, low delay and error resiliency. This paper addresses these demands by proposing a system with the integration of Network Coding (NC), user cooperation using Randomized Distributed Space-time Coding (R-DSTC) and packet level Forward Error Correction (FEC) under a one-way delay constraint. Simulation results show that the proposed scheme significantly outperforms both conventional direct transmission as well as R-DSTC based two-way cooperative transmission, and is most effective when the distance between the users is large.

With the increased popularity of mobile multimedia services, efficient and robust video multicast strategies are of critical importance. Cooperative communications has been shown to improve the robustness and the data rates for point-to-point transmission. In this paper, a two-hop cooperative transmission scheme for multicast in infrastructure-based networks is used, where multiple relays forward the data simultaneously using randomized distributed space time codes (RDSTC). This randomized cooperative transmission is further integrated with layered video coding and packet level forward error correction (FEC) to enable efficient and robust video multicast. Three different schemes are proposed to find the system operating parameters based on the availability of the channel information at the source station: RDSTC with full channel information, RDSTC with limited channel information, and RDSTC with node count. The performance of these three schemes are compared with rate adaptive direct transmission and conventional multicast that does not use rate adaptation. The results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, all three proposed randomized cooperative schemes outperform both strategies significantly as long as the network has enough nodes. Furthermore, the performance gap between RDSTC with full channel information and RDSTC with limited channel information or node count is relatively small, indicating the robustness of the proposed cooperative multicast system using RDSTC.

In this paper we study a two-hop cooperative transmission scheme where multiple relays forward the data simultaneously using Randomized Distributed Space Time Codes (R-DSTC). We propose to integrate this randomized cooperative transmission with layered video coding and packet level Forward Error Correction (FEC) to enable error resilient video multicast. Data rates in both hops as well as the FEC rate are adopted to maximize the video quality. Our results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, randomized cooperative scheme outperforms both strategies significantly.

In this paper, a cooperative multicast scheme that uses Randomized Distributed Space Time Codes (R-DSTC), along with packet level Forward Error Correction (FEC), is studied. For the source packets, two-hop transmission is considered, where a packet is transmitted first by the access point (AP), and then forwarded using R-DSTC by the nodes that receive the packet. On the other hand, parity packets are generated by the nodes that receive all the source packets correctly and are transmitted using R-DSTC. The optimum transmission rates for source and parity packets, as well as the number of parity packets required, are determined such that the video quality at all nodes is maximized. It is shown that this scheme can support a higher video rate than a previously developed R-DSTC based scheme where both source and parity packets go through a two-hop transmission, as well as non-cooperative direct transmission.

The exact expressions for symbol error probability and outage probability of randomized distributed space-time codes (RDSTC) under Rayleigh fading are derived. The diversity gain derived in the literature uses the Chernoff bound which may not be achievable in general. Utilizing the exact expression, the achievable diversity gain of RDSTCs is validated. The analytical expressions are verified by Monte-Carlo simulations.

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In this paper, an integration of layered video coding, packet level forward error correction, and two-hop relaying is proposed to enable efficient and robust video multicast in infrastructure-based wireless networks. First, transmission with conventional omni-directional antennas is considered where relays have to transmit in non-overlapping time slots in order to avoid collision. In order to improve system efficiency, we next investigate a system in which relays transmit simultaneously using directional antennas. In both systems, we consider a non-layered configuration, where the relays forward all received video packets and all users receive the same video quality, as well as a layered setup, where the relays forward only the base-layer video. For each system setup, we consider optimization of the relay placement, user partition, transmission rates of each hop, and time scheduling between source and relay transmissions. Our analysis shows that the non-layered system can provide better video quality to all users than the conventional direct transmission system, and the layered system enables some users to enjoy significantly better quality, while guaranteeing other users the same or better quality than direct transmission. The directional relay system can provide substantial improvements over the omni-directional relay system. To support our results, a prototype is implemented using open source drivers and socket programming, and the system performance is validated with real-world experiments.

Video multicast over wireless local area networks (WLANs) faces many challenges due to varying channel conditions and limited bandwidth. A promising solution to this problem is the use of packet level forward error correction (FEC) mechanisms. However, the adjustment of the FEC rate is not a trivial issue due to the dynamic wireless environment. This decision becomes more complicated if we consider the multi-rate capability of the existing wireless LAN technology that adjusts the transmission rates based on the channel conditions and the coverage range. In order to explore the above issues we conducted an experimental study of the packet loss behavior of the IEEE 802.11b protocol. In our experiments we considered different transmission rates under the broadcast mode in indoor and outdoor environments. We further explored the effectiveness of packet level FEC for video multicast over wireless networks with multi-rate capability. In order to evaluate the system quantitatively, we implemented a prototype using open source drivers and socket programming. Based on the experimental results, we provide guidelines on how to efficiently use FEC for wireless video multicast in order to improve the overall system performance. We show that the Packet Error Rate (PER) increases exponentially with distance and using a higher transmission rate together with stronger FEC is more efficient than using a lower transmission rate with weaker FEC for video multicast.

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. A promising solution to this problem is the use of packet level (FEC) mechanisms. However, the adjustment of the FEC rate is not a trivial issue due to the dynamic wireless environment. This decision becomes more complicated if we consider the multi-rate capability of the existing wireless LAN technology that adjusts the transmission rates based on the channel conditions and the coverage range. In this paper, we explore the dynamics of Forward Error Correction (FEC) schemes in multi-rate wireless local area networks. We study the fundamental behavior of a 802.11g network which already has embedded error correction in physical layer, under unicast and broadcast modes in a real outdoor environment. We then explore the effectiveness of packet level FEC over wireless networks with multi-rate capability. In order to evaluate the system quantitatively, we implemented a prototype using open source drivers, and ran experiments. Based on the experimental results, we provide guidelines on how to efficiently use FEC for wireless multicast services in order to improve the overall system performance. We argue that even there is a physical layer error correction, using a higher transmission rate together with stronger FEC is more efficient than using a lower transmission rate with weaker FEC for multicast.

Wireless video multicast enables delivery of popular events to many wireless users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In our previous work, we integrated layered video coding with cooperative communication to enable efficient and robust video multicast in infrastructure-based wireless networks. Through simulation and analysis, we showed that cooperative multicast improves the multicast system performance and the coverage area. In this work, we integrate the proposed system with packet level forward error correction (FEC) and evaluate the viability of the system in a realistic environment. We implement the system at the MAC layer and report the experimental results in a medium size (i.e., 8 stations) testbed. The experimental results confirm that the new cooperative MAC protocol for multicast, delivers superior performance.

Wireless video multicast enables delivery of popular events to many wireless users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. We propose to integrate layered video coding with cooperative communication to enable efficient and robust video multicast in infrastructure-based wireless networks. We determine the user partition and transmission time scheduling that can optimize a multicast performance criterion.

In this paper, we explore the use of directional antennas in relay transmission to improve the performance of video multicast with omni-directional relays in infrastructure- based wireless networks. We describe the system setup with directional relays and determine the user partition along with transmission time scheduling that can optimize a multicast performance criterion. We demonstrate that directional relays significantly improve the multicast system performance compared to omni-directional relays. Furthermore, it also provides larger coverage area.