Strict Quality Of Service Research Articles

Intent-driven orchestration of serverless applications in the computing continuum

Following the emergence of Internet of Things (IoT) and edge computing technologies and the development of distributed applications with strict Quality of Service (QoS) requirements, a transition is underway from centralized computing models to distributed computing continuum systems. The latter provide abstractions of the available resources, while they lead to the development of orchestration mechanisms with increased distributed intelligence and autonomy characteristics. These characteristics make the computing continuum suitable for managing serverless computing applications, considering their loose coupling with the resources and the need for reactiveness and efficiency to serve dynamic workloads in the edge and cloud part of the infrastructure. This paper presents an intent-based approach for the orchestration of a function chain over an edge and cloud infrastructure. We propose a Reinforcement Learning (RL) based autoscaling method that incorporates a reward function that aims to provide optimal utilization of resources and timely autoscaling of the functions, taking advantage of methods for workload prediction. Based on a high-level intent, the proposed optimization problem dictates the scheduling of the functions, minimizing the communication overhead and transformation cost while also considering the energy efficiency of the infrastructure. The proposed solution is evaluated with other state-of-the-art techniques for autoscaling and scheduling of serverless functions in a small edge infrastructure. Our solution provides 1.52% QoS violations with a slight increase in the deployed resources, while also significantly reducing the total cost and power consumption for the deployment of the function chain.

RAPID: Enabling fast online policy learning in dynamic public cloud environments

Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19%–43%.

A Multiple QoS Metrics-Aware Virtual Network Embedding Algorithm

Abstract As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.

Joint Optimization of Response Time and Deployment Cost in Next-Gen IoT Applications

The irruption of the Internet of Things (IoT) has attracted the interest of both the industry and academia for their application in intensive domains, such as healthcare. The strict Quality of Service (QoS) requirements of the next generation of intensive IoT applications require the QoS to be optimized considering the interplay of three key dimensions: 1) computing; 2) networking; and 3) application. This optimization requirement motivates the use of paradigms that provide virtualization, flexibility, and programmability to IoT applications. In the computing dimension, paradigms, such as edge or fog computing, software-defined networks in the networking dimension, along with micro-services architectures for the application dimension, are suitable for QoS-strict IoT scenarios. In this work, we present a framework, named Next-gen IoT Optimization (NIoTO), that considers these three dimensions and their interplay to place microservices and networking resources over an infrastructure, optimizing the deployment in terms of average response time and deployment cost. The evaluation of NIoTO in a healthcare case study reveals a response time speed up of up to 5.11 and a reduction in cost of up to 9% with respect to other state-of-the-art techniques.

Adaptive parameters selection for uplink grant-free URLLC transmission in 5G systems

Ultra-Reliable Low-Latency Communications (URLLC) is a key service of 5G systems that imposes very strict Quality of Service (QoS) requirements. Typically, URLLC applications require successful delivery of at least 99.999% of packets with a packet delivery time below 1 ms. To satisfy these requirements for uplink URLLC traffic, 5G specifications introduce a grant-free channel access method. It allows a base station (gNB) to select transmission parameters for each User Equipment (UE) in advance. The algorithm for transmission parameters selection shall take into account the following factors: (i) various UEs may have significantly different channel conditions that can change with time; (ii) if several UEs share common channel resources, their transmissions can interfere with each other; (iii) the gNB can use sophisticated decoding mechanisms, such as Successive Interference Cancellation (SIC), to mitigate the interference problem. In contrast to the existing studies that take into account only some of these factors, this paper proposes an algorithm that considers all three factors and adaptively selects the transmission parameters for each UE based on the desired signal and interference measurements available at the gNB. Numerical results obtained with NS-3 show that the developed algorithm significantly increases the network capacity for both periodic and sporadic URLLC traffic compared with the state-of-the-art algorithms.

CoScal: Multifaceted Scaling of Microservices With Reinforcement Learning

The emerging trend towards moving from monolithic applications to microservices has raised new performance challenges in cloud computing environments. Compared with traditional monolithic applications, the microservices are lightweight, fine-grained, and must be executed in a shorter time. Efficient scaling approaches are required to ensure microservices’ system performance under diverse workloads with strict Quality of Service (QoS) requirements and optimize resource provisioning. To solve this problem, we investigate the trade-offs between the dominant scaling techniques, including horizontal scaling, vertical scaling, and brownout in terms of execution cost and response time. We first present a prediction algorithm based on gradient recurrent units to accurately predict workloads assisting in scaling to achieve efficient scaling. Further, we propose a multi-faceted scaling approach using reinforcement learning called CoScal to learn the scaling techniques efficiently. The proposed CoScal approach takes full advantage of data-driven decisions and improves the system performance in terms of high communication cost and delay. We validate our proposed solution by implementing a containerized microservice prototype system and evaluated with two microservice applications. The extensive experiments demonstrate that CoScal reduces response time by 19%-29% and decreases the connection time of services by 16% when compared with the state-of-the-art scaling techniques for Sock Shop application. CoScal can also improve the number of successful transactions with 6%-10% for Stan’s Robot Shop application.

CMT-SCTP and MPTCP Multipath Transport Protocols: A Comprehensive Review

A huge amount of generated data is regularly exploding into the network by the users through smartphones, laptops, tablets, self-configured Internet-of-things (IoT) devices, and machine-to-machine (M2M) communication. In such a situation, satisfying critical quality-of-service (QoS) requirements (e.g., throughput, latency, bandwidth, and reliability) is a large challenge as a vast amount of data travels into the network. Nowadays, strict QoS requirements must be satisfied efficiently in many networked multimedia applications when intelligent multi-homed devices are used. Such devices support the concept of multi-homing. To be precise, they have multiple network interfaces that aim to connect and communicate concurrently with different networking technologies. Therefore, many multipath transport protocols are provided to multi-homed devices, which aim (1) to take advantage of several network paths at the transport layer (Layer-4) and (2) to meet the strict QoS requirements for providing low network latency, higher data rates, and increased reliability. To this end, this survey first presents the challenges/problems for supporting multipath transmission with possible solutions. Then, it reviews recent research efforts related to the concurrent multipath transmission (CMT) protocol and the multipath transmission control protocol (MPTCP). It reviews the latest research efforts by considering (1) how a multipath transport protocol operates (i.e., its functionality); (2) in what type of network; (3) what path characteristics it should consider; and (4) how it addresses various design challenges. Furthermore, it presents some lessons learned and discusses open research issues in multipath transport protocols.

Integration of Network Slicing and Machine Learning into Edge Networks for Low-Latency Services in 5G and beyond Systems

Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.

Achieving QoS for bursty uRLLC applications over passive optical networks

Emerging real-time applications such as those classified under ultra-reliable low latency (uRLLC) generate bursty traffic and have strict quality of service (QoS) requirements. The passive optical network (PON) is a popular access network technology, which is envisioned to handle such applications at the access segment of the network. However, the existing standards cannot handle strict QoS constraints for such applications. The available solutions rely on instantaneous heuristic decisions and maintain QoS constraints (mostly bandwidth) in an average sense. Existing proposals in generic networks with optimal strategies are computationally complex and are, therefore, not suitable for uRLLC applications. This paper presents a novel computationally efficient, far-sighted bandwidth allocation policy design for facilitating bursty uRLLC traffic in a PON framework while satisfying strict QoS (age of information/delay and bandwidth) requirements. To this purpose, first we design a delay-tracking mechanism, which allows us to model the resource allocation problem from a control-theoretic viewpoint as a model predictive control (MPC) problem. MPC helps in making far-sighted decisions regarding resource allocations and captures the time-varying dynamics of the network. We provide computationally efficient polynomial time solutions and show their implementation in the PON framework. Compared to existing approaches, MPC can improve delay violations by 15% and 45% at loads of 0.8 and 0.9, respectively, for delay-constrained applications of 1 ms and 4 ms. Our approach is also robust to varying traffic arrivals.

Deterministic Latency Bounded Network Slice Deployment in IP-Over-WDM Based Metro-Aggregation Networks

As a critical enabler of the next generation networks, network slicing can dynamically and flexibly create virtual networks leveraging necessary network resources for services with different quality of service (QoS) requirements. To satisfy the strict QoS requirements of some vertical industries, e.g., industry automation, the Deterministic Networking (DetNet) concept has been recently proposed to investigate deterministic service provisioning with bounds on service latency, loss, and jitter. A critical issue for providing deterministic services is how to allocate the right amount of resources that ensures the QoS requirements within a network slice. In this paper, we first obtain the amount of network resources needed for a network slice, using the stochastic network calculus (SNC) method, to ensure the end-to-end latency requirement under specific traffic demands. We then study the network slice deployment problem in an IP-over-WDM (wavelength division multiplexing) metro aggregation network and propose a heuristic with three different objectives: minimizing traffic hops, minimizing lightpaths and minimizing wavelengths, which can help network service provider to optimize network deployment under different considerations. The simulation results show the comparison of the resource utilization under different strategies.

Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

Providing QoS to High Performance Distributed Applications in Software-Defined Networks (SDNs)

Nowadays, there is an ever-increasing number of applications that have strict quality of service (QoS) requirements. The ability to define QoS requirements in traditional networks is limited due to the complex control of these environments. Modification of the existing infrastructure has high administrative costs, since the subsystem of each service component must have its resources managed by a centralized organization to guarantee users the level of QoS that each of them requires. The specification of quality of service (QoS) requirements in traditional networks is limited by the high administrative cost of these environments. Nevertheless, newer network paradigms, as Software-Defined Networks (SDNs), simplify and relaxes the management of networks. In this sense, SDN can provide a simple/effective way to develop QoS provisioning. This approach allows the specification of classes of service and also negotiates the QoS requirements between applications and the SDN network controller. The SDN controller, in turn, monitors the network and adjusts its performance through resource reservation and traffic prioritization. Have developed a QoS provision architecture for high performance distributed applications exploiting the capabilities of SDN. In this architecture, simple software components enable a process of message exchanging that includes reservation request and reservation confirmation. This procedure allows a network controller to configure QoS queues to individual packet flows. The developed a proof-of-concept of proposal and experimental results show that the additional routines present low overhead, whereas – for a given test application — observes a reduction of up to 47% in transfer times. Despite the specific scenario evaluated, results indicate our solution feasibility. Future works include the development of renegotiation and adaptation functions.

QoS Provisioning: Key Drivers and Enablers Toward the Tactile Internet in Beyond 5G Era

The Tactile Internet has become a revolution for Internet technology, greatly improving the transmission of skill sets (audio, video, text, and haptics) over communication channels compared with traditional triple-play data (audio, video, text). It is a strong candidate to support next-generation delay-sensitive and loss-intolerant smart applications. However, stringent requirements for the Tactile Internet, including ultra-low latency, ultra-high reliability, high availability, and ultra-security, present critical challenges to ensure Quality of Service (QoS). Consequently, several approaches have been proposed to meet these QoS requirements. This article reviews QoS provisioning approaches for the Tactile Internet. First, we present key concepts for the fifth-generation and beyond technologies, Tactile Internet, and haptic communication. Second, we discuss the Tactile Internet use cases along with strict QoS requirements. Third, we classify existing solutions, including haptic codecs, control system designs, hybrid schemes, and intelligent prediction models; provide in-depth discussion regarding these approaches to improve QoS for the Tactile Internet applications; and investigate strengths and weaknesses for each proposed solution. Finally, we present open research challenges and discuss potential future research avenues to realize the Tactile Internet services.

Preemptive admission control mechanism for strict QoS guarantee to life-saving emergency traffic in wireless LANs

The increasing usage of distributed emergency services (e.g. natural disaster or disaster caused by humans) in wireless local area networks requires the support of immediate channel access with strict quality of service (QoS) guarantee from a medium access control (MAC) protocol. The IEEE 802.11e/enhanced distributed channel access (EDCA) standard is the MAC enhancement for QoS. Unfortunately, 802.11e (EDCA) neither supports emergency traffic nor provides a strict QoS guarantee especially for a large number of users who report an emergency. To address this problem of achieving a strict QoS guarantee for emergency traffic, we previously developed and reported a multi-preemptive EDCA (MP-EDCA) protocol suitable for operating under low to medium traffic loads. However, MP-EDCA does not provide a strict QoS guarantee to life-saving emergency traffic (e.g. ambulance calls) in highly loaded networks. In this paper, we provide a solution to the problem of achieving a strict QoS guarantee to life-saving emergency traffic under high traffic loads. To this end, we propose a preemptive admission control (PAC) mechanism for MP-EDCA called PAC-MP-EDCA. The proposed PAC-MP-EDCA protects ongoing life-saving emergency traffic flows by giving high priority; thus, assures a QoS guarantee (in terms of lower packet delays) to life-saving emergency traffic when a high number of nodes require immediate channel access during emergency time. The priorities are set by carefully adjusting the short inter-frame space and slot-time in the emergency frames. The performance of PAC-MP-EDCA is evaluated by Riverbed Modeler simulation. Results obtained show that the proposed PAC-MP-EDCA achieved up to 98% lower MAC delays for life-saving emergency nodes and about 15% higher throughput than MP-EDCA under high traffic loads.

Network Slicing on 5G Vehicular Cloud Computing Systems

Fifth generation Vehicular Cloud Computing (5G-VCC) systems support various services with strict Quality of Service (QoS) constraints. Network access technologies such as Long-Term Evolution Advanced Pro with Full Dimensional Multiple-Input Multiple-Output (LTE-A Pro FD-MIMO) and LTE Vehicle to Everything (LTE-V2X) undertake the service of an increasing number of vehicular users, since each vehicle could serve multiple passenger with multiple services. Therefore, the design of efficient resource allocation schemes for 5G-VCC infrastructures is needed. This paper describes a network slicing scheme for 5G-VCC systems that aims to improve the performance of modern vehicular services. The QoS that each user perceives for his services as well as the energy consumption that each access network causes to user equipment are considered. Subsequently, the satisfactory grade of the user services is estimated by taking into consideration both the perceived QoS and the energy consumption. If the estimated satisfactory grade is above a predefined service threshold, then the necessary Resource Blocks (RBs) from the current Point of Access (PoA) are allocated to support the user’s services. On the contrary, if the estimated satisfactory grade is lower than the aforementioned threshold, additional RBs from a Virtual Resource Pool (VRP) located at the Software Defined Network (SDN) controller are committed by the PoA in order to satisfy the required services. The proposed scheme uses a Management and Orchestration (MANO) entity implemented by a SDN controller, orchestrating the entire procedure avoiding situations of interference from RBs of neighboring PoAs. Performance evaluation shows that the suggested method improves the resource allocation and enhances the performance of the offered services in terms of packet transfer delay, jitter, throughput and packet loss ratio.

A survey on 802.11 MAC industrial standards, architecture, security & supporting emergency traffic: Future directions

The IEEE 802.11-based Wireless Local Area Network (WLAN) has become a ubiquitous networking technology deployed around the world. IEEE 802.11 WLAN are now widely used for real-time multimedia applications (e.g. voice and video streaming) and distributed emergency services such as telemedicine, healthcare, and disaster recovery. Both time-sensitive applications and emergency traffic are not only characterized by their high bandwidth requirements, but also impose severe restrictions on end-to-end packet delays (i.e. response time), jitter (i.e. delay variance) and packet losses. In other words, time-sensitive applications and emergency services require a strict Quality of Service (QoS) guarantee. Medium Access Control (MAC) protocol is one of the key factors that influence the performance of WLANs. The IEEE 802.11e working group enhanced the 802.11 MAC to provide QoS support in WLANs. However, recent studies have shown that 802.11e Enhanced Distributed Channel Access (EDCA) standard has limitations and it neither supports strict QoS guarantee nor emergency traffic. Providing a strict QoS guarantee as well as supporting emergency traffic under high traffic loads is really a challenging task in WLANs. A thorough review of literature on QoS MAC protocols reveals that most QoS schemes have focused on either network throughput enhancement or service differentiation by adjusting Contention Window (CW) or Inter-Frame Spaces (IFS). Therefore, a research on developing techniques to provide a strict QoS guarantee as well as support for emergency traffic is required in such systems. To achieve this objective, a general understanding of WLANs is required. This paper aims introduce various key concepts of WLANs that are necessary for design, model and develop such framework. Our main contribution in this paper is the QoS for IEEE 802.11 WLAN and MAC protocols for supporting industrial emergency traffic over network and future directions.

Modeling and Performance Evaluation of Multi-Class Queuing System with QoS and Priority Constraints

Many service providers often categorize their users into multi-classes, depending on their service requirements. Each class has strict quality of service (QoS) demands (e.g., minimum required service rate or transfer time) that must be ensured throughout its service. In some cases, priorities are also assigned in a multi-class user’s environment to ensure that the important class user shall be serviced first. In this paper, we have developed a novel Markov chain based analytical model to investigate and evaluate a multi-class queuing system with a strict QoS requirement and priority constraints. Experimental analysis is conducted for two users classes, i.e., class-1 (may be free/student users) and class-2 (may be paid/research users). Each class requests have strict QoS requirements in terms of the minimum required rate (MRR) that must be ensured throughout its lifetime once the request is admitted into the system. Secondly, class-2 requests have preemption priority over class-1, i.e., if there is no room for newly arriving class-2 requests, then one or more active flows of class-1 can be ejected in order to accommodate high-class requests. Model results are validated through simulation results and performance measures of our interest include blocking probability (BP) of individual classes and the overall system, effect of higher-class jobs on lower-class jobs, and link capacity utilization. The proposed model can be instrumental in developing advanced connection admission control (CAC), efficient resource dimensioning, and capacity planning of the queuing system.

ODeSe: On-Demand Selection for multi-path RPL networks

The Internet of Things (IoT) has become ubiquitous due to its flexibility, ease-of-use, and reduced cost. As a consequence, the industry is adopting these systems in its transformation into Industry 4.0. However, the strict Quality of Service (QoS) requirements of the industry are not met with the default best-effort provisions of the IoT. Most industrial applications require strict guarantees in terms of end-to-end network reliability and latency. For instance, consecutive packet losses can lead to communication disruptions in supply chain systems. Therefore, adaptations are being made to fulfill these requirements with the IEEE Std 802.15.4-2015 Time slotted Channel Hopping (TSCH) link-layer standard and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) standard at the Internet Engineering Task Force (IETF). However, even by employing such industrial protocols, it is still difficult to achieve the expected QoS levels. Considering that RPL constructs and maintains a single-path from a source to a destination if there are potential issues on this path (e.g., queue overflow, variable wireless link quality) packets may suffer unexpected delays and even drops. If we consider a multi-path implementation where each node can replicate a packet into several paths, the transmission reliability improves since each packet copy is used to forward the packet information. However, uncontrolled replication can lead to network flooding, resulting in excessive power consumption. In this article, we present On-Demand Selection (ODeSe), a novel multi-path routing algorithm, which improves our previous work, the Common Ancestor (CA) algorithms, by selecting the most suitable upward forwarders. Moreover, we use the Packet Automatic Repeat reQuest, Replication and Elimination, and Overhearing (PAREO) functions in order to improve network reliability and availability. In order to control the number of relay nodes during a transmission, ODeSe forces each node of the same relay level to select the same Preferred Parent (PP) and the same Alternative Parent (AP). Thus, we address the trade-off between the total number of traversed nodes in the network and high reliability. Using the Cooja network simulator running the Contiki OS, we compare ODeSe against single-path RPL and multi-path RPL with different alternative parent selection algorithms. The results demonstrate that ODeSe outperforms single-path RPL in terms of reliability, and multi-path RPL in terms of energy consumption while maintaining a 99.14% packet delivery ratio.

Smart Mode Selection Using Online Reinforcement Learning for VR Broadband Broadcasting in D2D Assisted 5G HetNets

As an emerging broadband service pattern in the 5G era, VR broadcasting needs a considerable amount of bandwidth and strict quality of service (QoS) control. The traditional eMBMS or enTV transmission mode in HetNets consisting of macro cells and small cells cannot bring about a good trade-off between broadband performance and resource utilization for VR broadcasting service. D2D multicasting applied to VR broadcasting can improve the performance of edge users and resource utilization. Motivated by the rapid development of AI techniques, this paper proposes a novel hybrid transmission mode selection based on online reinforcement learning to address this problem. Each VR broadband user can be associated by one of the three modes: macrocell broadcasting, mmWave small cell unicasting and D2D multicasting. This paper first models this intelligent mode decision process as a problem to pursue the optimal system throughput. Then, an online machine learning-based method is proposed to solve this problem, which consists of a fast D2D clustering module based on unsupervised learning and a smart mode selection module based on reinforcement learning. The simulation results verify that the WoLF-PHC and Nash Q-learning perform better than other algorithms in large-scale scenarios and small-scale scenarios, respectively. The proposed intelligent transmission mode selection can also achieve larger VR throughput than traditional broadcasting strategies with a good balance between broadband performance and resource utilization.

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Over the last few years, the evolution of network and user handsets’ technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and added-value services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.