Mobile application classification is essential for advanced network management and application-based QoS policy enforcement in future, AI-enhanced, beyond 5G and 6G mobile networks. This article proposes to use AI methods to categorize applications as functional types (e.g., Video, Audio, Browsing) despite encryption and limited labeled data. We tackle these challenges through unsupervised representation learning, which maximizes the use of abundant unlabeled data in mobile networks. Due to the distributed nature of beyond 5G and 6G networks, we use this method in federated learning scenarios and compare it to the centralized ones. Our findings highlight that unsupervised learning improves model performance, especially with scarce labeled data. Additionally, federated learning provides effective results as compared to centralized methods.

Mobile networks are rapidly expanding, and there is an increasing demand for seamless connectivity. Detecting whether a user is indoors or outdoors is pivotal in optimizing network performance and enhancing user experience. This paper proposes a semi-supervised learning method using a Long Short-Term Memory Autoencoder (LSTM-AE) that detects the user's environment. It uses mobile network radio signal data of real users. The LSTM Autoencoder learns to capture the underlying structure of the data and identify patterns that distinguish indoor from outdoor environments. Three key features are used to train the model: Reference Signal Received Power (RSRP), Channel Quality Indicator (CQI), and Timing Advance (TA). Results show that the LSTM-AE model achieves a high accuracy of 84% and an F1 score of 89%. In our approach, we achieve a substantial reduction of 34.14% in the requirement for labeled data compared to traditional methods that primarily rely on fully supervised learning. By diminishing the dependence on labour-intensive and time-consuming data labelling processes, this improvement significantly enhances the overall efficiency of the machine-learning process.

Optimizing networks to meet user needs has been a longstanding goal for the various key players in the network sector. To this end, a large number of studies have addressed the case of classifying network traffic into a set of activities (e.g., streaming) and applications (e.g., Spotify). Nonetheless, the fast-paced growth of the digital market has favored the advent of new consuming habits such as the simultaneous performance of multiple activities. This concept is referred to as multi-activity situations or media multitasking. Conceiving solutions that can cope with these emerging consuming patterns may enable network operators and service providers to better adapt their network management solutions and commercial plans. In this paper, we propose a novel approach that can deal with a challenging scenario comprising both single-activity and multi-activity situations. The proposed approach pre-processes a network trace over a time-window and then determines to which situation type it belongs. Furthermore, it identifies the type of the activities being performed and the applications being used (e.g., chat on Facebook & streaming on Spotify). Our experiments highlighted that our solution is able to achieve a satisfactory level of performance despite the complexity of the scenario that we target. Indeed, our obtained results are comparable to state-of-the-art techniques addressing less challenging scenarios that involve only single-activity situations.

Network traffic classification is an active research field that acts as an enabler for various applications in network management and cybersecurity. Numerous studies from this field have targeted the case of classifying network traffic into a set of single-activities (e.g., chatting, streaming). However, the proliferation of internet services and devices has led to the emergence of new consuming patterns such as multi-tasking that consists in performing several activities simultaneously. Recognizing the occurrence of such multi-activity situations may help service providers to design quality of service solutions that better fit users' requirements. In this paper, we propose a framework that is able to recognize multi-activity situations based on network traces. Our experiments showed that our solution is able to achieve promising results despite the complexity of the task that we target. Indeed, the obtained multi-activity detection performance is equivalent or often surpasses state-of-the-art techniques dealing with only a single activity.

This paper demonstrates the feasibility of an hybrid/semi-supervised classification method for detecting the environment of an active mobile phone, based on both labeled and unlabeled cellular radio data. Precisely, we provide answers to the following question: what is the environment of the mobile user when it is/was experiencing a mobile service/application: indoor or outdoor? Implementing this method within the mobile network is interesting for mobile operators since it has low complexity, is less human intrusive (minimal intervention of mobile users) and more accurate. The semi-supervised classification algorithm learns to identify the environment using large and real collected 3GPP signals measurements. As compared to existing work, in addition to existing parameters used for classification, we propose to also use a radio metric called Timing Advance. It is computed within the mobile network. We empirically validate the innovative semi-supervised algorithm using new real-time radio measurements, with partial ground truth information, gathered daily, weekly, monthly, from indoor and outdoor locations and from multiple typical and diversified environments crossed by mobile users. The study confirms the effectiveness of the proposed scheme compared to the existing supervised classification methods including SVM and Deep Learning.

Future mobile networks promise to be more intelligent and to guarantee a better service for users. This intelligence can be highly accentuated by cognition of mobile users' behavior and conditions. The user speed is an important element of user profile. We are interested in real time speed profiling by detecting speed range of an active user. We refer to this as Mobile Speed Profiling (MSP) of users. Indeed, performing MSP can notably improve the self-adaptation and self-optimization capabilities of these networks. It can help mobile network in resource management and handover management. This paper introduces a Deep Learning based solution to automatically construct the MSP of a mobile user. We empirically evaluate the effectiveness of our approach using real-time and highly representative radio data that best captures the real daily movements of users. This data includes ground truth information and the whole dataset has been gathered massively from many diversified mobility situations. Results show that the profiling of UE's speed with fine granularity or on multiple ranges can be achieved with high accuracy on real data measured in heterogeneous deployments for 5G networks.

Detecting whether a mobile user is indoor or outdoor is an important issue which significantly impacts user behavior contextualization and mobile network resource management. Indoor Outdoor Detection (IOD) can be performed within mobile networks or in user terminals. Implementing IOD in mobile networks is interesting for operators as it is less costly, easier to deploy, and more energy efficient with centralized computing. This paper investigates hybrid/semi-supervised Deep Learning-based methods for detecting the environment of an active mobile phone user. They are based on both labeled and unlabeled large real radio data obtained from inside the network and from 3GPP signal measurements. We empirically evaluate the effectiveness of the semi-supervised learning methods using new real-time radio data, with partial ground truth information, gathered massively from multiple typical and diversified locations (indoor and outdoor) of mobile users. We also present an analysis of such schemes as compared to the existing supervised classification methods including SVM and Deep Learning.

Cognition of user behavior can be seen as an efficient tool for automation of future mobile networks. As a matter of fact, it amplifies the intelligence of autonomic networks in a sense that the network is more aware of the operational context. However, predicting the context of mobile users is a prerequisite for inferring the user behavior. This paper deals with the user behaviour modeling. The model includes the prediction of two main features related to mobile user context: the environment and the mobility. Practically, the question is how and where the mobile user consumes the mobile services. We investigate Deep Learning based methods for simultaneously detecting the environment and the mobility state. We empirically evaluate the effectiveness of the proposed methods using real-time radio data, which has been massively gathered from multiple diversified situations of mobile users.

The upcoming mobile core network, which will be based on Virtual Network Functions (VNF), will face an increase of data traffic on both data and control planes. This is due to the increase of the number of connected devices and the newly 5G supported-services like IoT, Connected Health Care etc. Therefore dynamic and accurate scalability techniques should be envisioned in order to answer the needs, in term of resource provisioning, without degrading the Quality Of Service (QoS) already offered by hardware based core networks. Although provisioning new resources is easier as it is a matter of software deployment, the strategy to use (when to scale and how much to scale) remains complex. In this paper we propose scaling techniques based on neural networks to forecast the upcoming load. Hence scheduling the resource provisioning should be in a manner that all the needed resources will be deployed and active when the load increases. In the same way, it will scale-in the unneeded resources when the traffic load decreases. The proposal is tested via discrete event simulations using a traffic load dataset provided by a Network Operator. The results show clearly the robustness of our proposal compared to a threshold-based scaling technique.

Relaying packets through multi-hop ad-hoc networks and the scarcity of wireless resources can severely deteriorate the quality of service. As a result, one of the major challenges in video streaming over ad-hoc networks is enhancing users experience and network utilization. An extension of H.264 standard, named scalable video coding (SVC), enables smooth adaptation of video quality to networks' status. In this paper, we formulate the problem of optimizing quality of experience (QoE) for SVC under time-constraints. The problem itself is a mixed integer linear programming which is NP-hard, and therefore we propose a heuristic algorithm to solve it. Simulation results show that the proposed algorithm can provide the similar video quality as optimal solutions with shorter calculation time.

This paper presents a QoE-based multipath routing algorithm for scalable video coding (SVC) over multichannel wireless mesh networks (WMN). The availability and quality of links in WMN significantly depends on not only the positions of neighbors but also the interference between links. In video streaming, the quality perceived by the users is the key to success. Consequently, enhancing experience of users and network utilization in these networks is an interesting challenge. In this paper, we utilize multicommodity model to formulate the problem of streaming SVC video over multichannel WMN. Then, we propose a heuristic algorithm to speed up searching solution procedure. Simulation results demonstrate that the proposed algorithm can obtain a near-optimal solution within a short calculation time.

The major challenge in ad-hoc network is multi-hop paradigm. Moreover, multimedia streaming over ad-hoc networks increasingly emerges since it can be useful in various practical cases. In video streaming, previous studies provided quality of service (QoS)-based methods that focused on the value of technical parameters such as bandwidth, jitter, delay, etc. It is however not perfectly correlated to users' experience. In this paper, we propose a routing mechanism based on optimized link state routing (OLSR) to guarantee a good quality of experience (QoE) of users. The pseudo-subjective quality assessment (PSQA) is adopted to estimate mean opinion scores (MOS), then this MOS value will be exploited by the source for selecting the appropriate path. In addition, an event-triggered based on the MOS value is exploited to provide more relevant information. The results show that the proposed scheme outperforms other OLSR-based routing protocols particularly in heavy load and moderate mobility scenario.

In-vehicle multimedia applications are gaining interest since recent years. However, the high loss rate caused by high mobility in vehicular ad-hoc networks (VANETs) imposes several challenges in multimedia transmission. Moreover, in the context of multimedia, the quality of service (QoS)-based approaches assess the quality of streaming services through network-oriented metrics while the concept of quality of experience (QoE) is built upon the perception of users. Consequently, adopting QoE metric to control multimedia streams in VANETs is interesting and relevant. In this paper, a QoE-based routing protocol for video streaming over VANETs is proposed. By taking the mean opinion score (MOS) into account for path selection, a better performance can be achieved. Simulation results demonstrate how the proposed scheme improves the performance of video streaming applications in VANETs.

Quality of Experience (QoE) is the key to success for multimedia applications and perceptual video quality is one of the important component of QoE. A recent video encoding scheme called Scalable Video Coding (SVC) provides the flexibility and the capability to adapt the video quality to varying network conditions and heterogeneous users. In this paper, we focus on SVC multicast over IEEE 802.11 networks. Traditionally, multicast uses the lowest modulation resulting in a video with only base quality even for users with good channel conditions. To optimize QoE, we propose to use multiple multicast sessions with different transmission rates for different SVC layers. The goal is to provide at least the multicast session with acceptable quality to users with bad channel conditions and to provide additional multicast sessions having SVC enhancement layers to users with better channel conditions. The selection of modulation rate for each SVC layer and for each multicast session is achieved with binary integer linear programming depending on network conditions with a goal to maximize global QoE. Results show that our algorithm maximizes global QoE by providing highest quality videos to users with good channel conditions and by guaranteeing at least acceptable QoE for all users.

In the objective of streaming the video to the end-user with the best possible quality, in this paper we propose an approach that couples the SNR scalable video coding (SVC) extension of H264/AVC, with the path diversity provided by video distribution network (VDN). Our method adapts to the heterogeneity of end-users using the scalable video coding. Moreover, it adapts to network bandwidth fluctuation by observing the changes of the available bandwidth over the multiple overlay paths, and updating the streaming strategy accordingly.

The multimedia streams over the wireless access networks has increased dramatically over the past few years. Since the wireless link is featured with restricted bandwidth and limited resources, increasing demand of real-time applications imposes challenges on wireless networks to provide Quality of Service (QoS). Plenty of schemes were proposed to provide QoS, among which the admission control is a typical method. However, admission control mechanisms only consider the case at the network entering phase. After the admission, another mechanism is needed to continue to monitor the ongoing traffic and to react to fluctuation of resource consumption during the connection. Therefore, other than only using admission control schemes, which try to restrict access to the network, schemes to control on-going best-effort background are proposed in this paper. The proposed dynamical halting and resuming schemes of background traffic provide a finer way to allocate resources for video streams. It is shown via extensive simulations that proposed schemes can provide direct and solid enhancement of Quality-of-Experience (QoE) for video subscribers, and achieve good tradeoff between video quality and bandwidth utilization.

In the domain of video streaming, Scalable Video Coding (SVC) comes as a solution that adapts to network bandwidth fluctuation and to terminals heterogeneity. The multilayer-coding criterion of SVC has increased its correlation with the streaming over multiple paths. However, an important challenge that we still need to face is how to maximize the overall video quality in a way that satisfies the end-user, without greedily consuming the network resources. To this purpose, in this paper we aim at providing a powerful system that is based on the Quality of Experience (QoE) evaluations, to adjust the streaming of SVC video flows over multiple paths. Our method dynamically selects the best overlay paths using the available bandwidth estimations. Keeping/updating the selected paths is then automatically done based on the feedback of the quality as it is perceived by the end-user. For evaluating the QoE at the recipient, we use an SVC-compatible module of the hybrid Pseudo-Subjective Quality assessment (PSQA) tool. Results show how our method can guarantee the deliverance of the best possible video quality, while keeping the balance between end-user requirements and network bandwidth wise consumption.

Deployment of next-generation network (4G) begins to spread throughout the world. With variety of network technologies and QoS restrictions on emerging applications; it becomes difficult for a user to select the best access network to request for connection. Even though many schemes have been proposed in the literature but very few of them take into account quality of experience (QoE) perceived by user for making decision. As QoE represents perception experienced by the user, it is thus an essential indicator for network evaluation, especially with multimedia communications nowadays. Therefore, in this paper we propose a novel network selection mechanism that takes quality of experience into consideration for decision making. It is a user-baaed and network-assisted approach thus a compromise solution between user and network benefit. The main idea is to use quality of experience of ongoing users in candidate networks as an indicator to select the best network for connection. We have implemented and tested our mechanism in network simulator NS-2. The obtained results illustrate that with a QoE-aware mechanism we can significantly improve user experience of mobile node and load balancing between networks.

With widespread use of multimedia communication, quality of experience (QoE) progressively becomes an important factor in networking today. Besides, multimedia applications can be supported under various technologies, including wired and wireless networks. Among them, Universal Mobile Telecommunications System (UMTS) is one of the most popular technology thanks to its support on mobility. Improved with a new access method (High Speed Downlink Packet Access or HSDPA), it can provide higher bandwidth and enable wider range of services including multimedia applications. In UMTS, different categories of traffic are specified along with their characteristics. Best effort traffic has been specified with less priority because it has fewer constraints. On the other hand, real-time multimedia traffic such as streaming video or VoIP are more sensitive to network condition changes, hence special treatment (e.g. QoS scheduler) is needed in order to achieve user satisfaction. According to the literature, most of scheduling mechanisms mainly take into account signal quality and fairness and do not consider user perception. In this paper, we propose a novel approach, QoE-aware scheduler that takes quality of experience into account when making scheduling decisions. Compared to other existing schedulers, QoE-aware approach has reached a profitable performance in terms of user satisfaction, throughput, and fairness.

Nowadays, wireless networks have been deploying everywhere, with IEEE 802.11 as the most popular standard. However, wireless resources are scarce and wireless condition varies often. These limitations are crucial for applications with tight QoS requirements such as video or voice over IP. To cope wi

The deployment of real-time and multimedia applications over wireless LAN has gained a growing interest in this last decade. These applications, e.g. video streaming, require tight guarantee on quality of service (QoS). Also, they use multicast communication in order to reduce the bandwidth consumption. However, in WLAN, multicast packets are sent with the basic rate (lowest rate); which results in capacity wasting because of longer channel occupancy. Moreover the lack of feedback mechanism makes it difficult to deal with reliability or service quality. In this paper, we propose to rely on the WLAN multi-rate capability, in order to transmit multicast packets with higher and dynamic rate than the basic rate. Unlike other existing protocols that use a static-threshold to decide when to change transmission rate, we propose a novel dynamic rate-adaptation mechanism based on quality of experience (QoE), namely Q-DRAM. According to the clients' feedback on QoE, we adapt the multicast rate: (i) when users had bad QoE we reduce the multicast transmission rate; (ii) when users had good QoE, we increase the multicast transmission rate. Simulation results show that Q-DRAM increases the wireless channel utilization and maximizes users' QoE, compared to existing solutions as well as to the IEEE 802.11 standard.

Widespread use of wireless networks nowadays raises many challenging issues to be explored. With increasing of multimedia traffic, quality of experience (QoE) needs to be satisfied at users while overall performance needs to be maintained at networks. In order to achieve these goals, the use of network selection mechanism is inevitable. When several access points are present, user should select the best available network while trying to keep load balanced between access networks. In this paper, we present a user-based and network-assisted scheme for network selection in wireless IEEE 802.11 technology. By providing users in decision making process with relevant information about the networks, the proposed solution keeps compromising advantage for both user and network. Our mechanism is based on a technique called pseudo-subjective quality assessment (PSQA), which is used basically to measure quality of experience perceived by users. We propose a new scheme where PSQA tool is used to assist in terminal-centric network selection. We explain the scheme and illustrate its efficient performance compared to a signal-based mechanism.

Widespread use of wireless networks nowadays raises many problems for service providers in managing their resources. These problems are caused mainly by restricted bandwidth and variable radio condition in this type of network. Moreover, with the emergence of multimedia traffic and its requirements in terms of quality, admission control is hence an inevitable choice to optimize network resources while maintaining high service quality at users. In this paper, we propose an admission control mechanism based on quality of experience (QoE) perceived by users. The human QoE is obtained by a tool called pseudo subjective quality assessment (PSQA), which is based on statistic learning using random neural network (RNN). Instead of relying on technical parameters such as bandwidth, loss, or latency, which do not correlate well with human perception, our scheme is based on mean opinion score (MOS) but without interaction from real humans. The simulation results demonstrate the better performance of our proposition compared to the loss-based approach regarding user satisfaction evaluated by achieved QoE at user and bandwidth utilization of the network evaluated by good put.

High speed downlink packet access (HSDPA) is a packet-based data service in UMTS networks that supports data rates of several Mbit/s, making it suitable for data applications ranging from file transfer to multimedia streaming. In spite of the fairly high data rates that HSDPA offers, the shared downlink radio channel used in HSDPA is a challenging environment for delay- and loss-sensitive applications. It is relatively easy to partition resources over wired links with known link capacity. Nevertheless, such partitioning is complex for wireless links with variable capacity; due to the dynamic nature of the link, it is difficult to effectively allocate the resources (i.e., time slots) between users who may be paying different prices for different services. In this paper we study the general problem of resource partitioning for shared wireless links like HSDPA. We consider different QoS classes or user groups, and the use of MAC-layer scheduling for resource partitioning between groups and between individual users. We propose a variant of an existing HSDPA scheduler called required activity detection (RAD) scheduler. The proposal is studied using a detailed UMTS/HSDPA simulator.

Configuration management presents several challenges to test suite execution in interoperability testing. Several nodes have to be rearranged from test to test in a reliable and efficient way. This work proposes a novel methodology based on the replacement of underlying networking technologies to transparently automate the process. The methodology is presented and initial results are exposed

Many networked applications use end-to-end congestion control protocols to avoid the occurrence of congestion collapse phenomena. However, such transport protocols may suffer from link underutilization when there are wireless links along the path: the inability to distinguish wireless losses from congestion losses often results in unnecessary throughput decreases. Since networks are becoming increasingly heterogeneous, a mix of wired and wireless links, it is important to have some means of identifying the cause of a packet loss and to adapt the response of the transport protocol. In this paper, we present an end-to-end solution to this problem and propose a novel method for wireless loss estimation in IP DiffServ networks (WLED). WLED aims at enhancing the performance of equation-based rate control protocols for multimedia applications, by means of directly estimating the wireless and congestion loss rates. We discuss the integration of WLED with a recent, equation-based rate control protocol and we evaluate WLED's performance through simulation. Our results illustrate a new benefit of using DiffServ-like differentiation mechanisms for multimedia streaming in wireless networks, like UMTS, that support quality of service (QoS).