Quality Of Experience Requirements Research Articles

Dependent Task Offloading and Resource Allocation via Deep Reinforcement Learning for Extended Reality in Mobile Edge Networks

Extended reality (XR) is an immersive technology widely applied in various fields. Due to the real-time interaction required between users and virtual environments, XR applications are highly sensitive to latency. Furthermore, handling computationally intensive tasks on wireless XR devices leads to energy consumption, which is a critical performance constraint for XR applications. It has been noted that the XR task can be decoupled to several subtasks with mixed serial–parallel relationships. Furthermore, the evaluation of XR application performance involves both subjective assessments from users and objective evaluations, such as of energy consumption. Therefore, in edge computing environments, ways to integrate task offloading for XR subtasks to meet users’ demands for XR applications is a complex and challenging issue. To address this issue, this paper constructs a wireless XR system based on mobile edge computing (MEC) and conducts research on the joint optimization of multi-user communication channel access and task offloading. Specifically, we consider the migration of partitioned XR tasks to MEC servers and formulate a joint optimization problem for communication channel access and task offloading. The objective is to maximize the ratio of quality of experience (QoE) to energy consumption while meeting the user QoE requirements. Subsequently, we introduce a deep reinforcement learning-based algorithm to address this optimization problem. The simulation results demonstrate the effectiveness of this algorithm in meeting user QoE demands and improving energy conversion efficiency, regardless of the XR task partitioning strategies employed.

A digital twin-based energy-efficient wireless multimedia sensor network for waterbirds monitoring

Wetlands play a critical role in maintaining the global climate, regulating the hydrological cycle, and protecting human health. However, they are rapidly disappearing due to human activities. Waterbirds are valuable bio-indicators of wetland health, but it is challenging to monitor them effectively. Wireless Multimedia Sensor Networks (WMSNs) offer a promising technology for monitoring wetlands. Nonetheless, these networks are constrained in terms of energy, and also encounter challenges associated with large-scale deployments under natural environmental conditions. These conditions introduce harsh circumstances that may not have been anticipated during the pre-deployment testing phase. This paper proposes a Digital Twin (DT) based energy-efficient WMSN monitoring system specifically tailored for waterbirds in wetlands. The system utilizes a unique approach that combines local audio identification and image compression with DT technology to optimize network performance and minimize energy consumption. To reduce unnecessary image transmissions, the system employs a real-time, low-complexity local audio identification phase before triggering image capture. A denoising step is employed to achieve highly accurate bird recognition despite surrounding noises. Each image undergoes a low-complexity compression scheme prior to transmission, further enhancing energy efficiency. To enhance the system’s overall efficiency and effectiveness, DT technology is integrated to create real-time replicas of the WMSN and the monitoring application. A synergistic interaction between the two DTs enables cooperative data-making decision that ensures both QoS (Quality of Service) and QoE (Quality of Experience) requirements are met. Transmission rate control is done using a fuzzy logic decision-making technique. Real-time feedback provides rapid and accurate analysis of the current state of the WMSN, allowing for dynamic adjustments. The ”what-if scenarios” feature of the implemented DTs has been effectively leveraged to find the most suitable settings for the controller. The effectiveness and performance enhancements achieved by integrating DT into our WMSN-based surveillance system are validated through comprehensive experiments in scenarios that correspond to a real-world wetland. Comparative analyses demonstrate the undeniable benefits of the DT-integrated system compared to a conventional WMSN-based surveillance setup. In particular, the results demonstrate the system’s superior performance in terms of energy efficiency, real-time monitoring capabilities, and ability to handle multiple video sources.

Multi-codec rate adaptive point cloud streaming for holographic-type communication

Point cloud videos play a crucial role in immersive applications enabled by holographic-type communication, which has been identified as an important service for 6G and beyond wireless systems and the metaverse. The significant volume of point cloud video demands efficient compression and transmission techniques to support the Quality of Experience (QoE) requirements of real-time immersive applications. A few Point Cloud Compression (PCC) techniques, such as MPEG PCC and Draco, have emerged in recent years, and studies have shown that each technique has its strengths and weaknesses under different system settings. This paper proposes a multi-codec rate adaptive point cloud streaming method to satisfy the QoE requirements of interactive and live applications considering available system resources. The proposed method leverages three common PCC techniques: MPEG V-PCC, MPEG G-PCC, and Draco. The performance of each PCC technique is evaluated under various test conditions, and then estimation models are constructed to predict the bit rate, the decoding time, and the quality of the reconstructed point cloud. Based on the user's quality requirements and available computational and communication resources, the proposed streaming method selects a codec along with appropriate compression parameters that can provide the minimum latency for streaming. Evaluation results demonstrate that the proposed method can provide better QoE than benchmark methods under various bandwidth and computation scenarios.

Slicing4Meta: An Intelligent Integration Architecture with Multi-Dimensional Network Resources for Metaverse-as-a-Service in Web 3.0

As the next-generation Internet paradigm, Web 3.0 encapsulates the expectations of immersive and interactive experiences in a decentralized manner. Metaverse, a virtual world interacting with the physical world, is becoming one of the most potential applications to push forward with Web 3.0. In the Metaverse, users expect to tailor immersive and interactive experiences by customizing real-time Metaverse services (e.g., augmented/virtual reality and digital twin) in the threedimensional virtual world. Nevertheless, there are still no unified solutions for the Metaverse services in terms of orchestration and management. Therefore, continuous and seamless upgrading of network systems is essential yet challenging to support Metaverse services. In this paper, to provide scalable solutions for tailoring Metaverse services, we propose a new concept, named Metaverseas- a-Service (MaaS), where users only need to purchase the MaaS models that are needed (e.g., computing, communications, and data resources) to achieve their required Metaverse services. Furthermore, to unify the orchestration and management of MaaS models, we propose a novel architecture, called Slicing4Meta, to customize Metaverse services by integrating MaaS models and the associated multi-dimensional resources. Additionally, we propose the classification for typical Metaverse services based on the quality of experience (QoE) requirements and illustrate how to fulfill the QoE requirements under the Slicing4Meta architecture. We then illustrate a virtual travel study case, in which we examine the relationship between the QoE and the multi-dimensional resources by quantitatively modeling the QoE of Metaverse users. Finally, we discuss some open challenges of Slicing4Meta and propose potential solutions to address the challenges.

D2OP: A Fair Dual-Objective Weighted Scheduling Scheme in Internet of Everything

In times of the Internet of Everything (IoE), the power of the Internet is growing exponentially, followed by a surge in the number of network requests. The conflict between people’s high requirements for quality of experience (QoE) and limited computing resources are becoming increasingly prominent. Therefore, an appropriate offloading method is required to better ease this conflict. In this paper, a highly efficient scheduling architecture of information processing under the big data flow of the IoE is proposed to enhance the scheduling performance. First, we construct a dual-channel processing model to describe the entire data flow and node devices. Second, we carefully consider the choice of weighting method to better find a balance between dual objectives. Third, a dual-objective deep Q-network (DQN)-based offloading algorithm with principal component analysis weighting method (D2OP) is proposed to collaboratively minimize task response time and machine load in a more reasonable allocation. To verify the performance of the D2OP, a series of experiments are conducted from multiple angles. The experimental results demonstrate its better performance than the three comparison algorithms in reducing response time, load balance, and increasing task success ratio.

Toward Supporting XR Services: Architecture and Enablers

Emerging cross-reality (XR) applications, including holography, augmented, virtual, and mixed reality, are characterized by unprecedented requirements for Quality of Experience (QoE), largely exceeding those currently attainable. To cope with these requirements, noticeable efforts and a number of initiatives are ongoing to enhance the current communications technologies, especially in the direction of supporting ultralow latency and increased bandwidth. This work proposes an architecture that puts together the key enablers to support future XR applications, highlighting the shortcomings of existing technologies and leveraging the ongoing innovations. It demonstrates the feasibility of the proposed architecture by describing the processes driving the platform with relevant use case scenarios, and mapping the envisioned functionality to existing tools.

Graph neural networks for representing multivariate resource usage: A multiplayer mobile gaming case-study

The emergence of Multiplayer Mobile Gaming (MMG) applications is intertwined with a plethora of Quality of Service and Quality of Experience requirements. Resource usage prediction can provide valuable insights into the corresponding orchestration and management process in the form of several proactive functionalities in resource scaling, service migration, task offloading and scheduling. These processes are crucial in the Cloud and Edge environments exploited by MMG applications. Thus, producing accurate resource usage predictions concerning these types of applications is of paramount importance. To that end, we propose a resource usage representation paradigm based on Graph Neural Networks (GNNs). The novelty of this approach is based on the process of leveraging the dependencies that exist among the various types of computational resources. Furthermore, we expand upon this representation approach to develop a GNN-based Encoder-Decoder model that caters to the complexities of resource usage and can provide multi-step resource usage predictions. This model is compared against numerous well-established Encoder-Decoder and Deep Learning prediction models to assess its efficiency. Finally, the proposed model is incorporated in a proactive Horizontal Autoscaling solution that manages to outperform a standard reactive Horizontal Autoscaling approach in the context of a large-scale simulation, in terms of various performance metrics, while keeping the volume of the required computational resources to a minimum. The findings of this work showcase the importance of developing novel approaches in order to represent resource usage and the numerous benefits in the context of application performance and resource consumption that may derive from such scientific endeavors.

A Data Driven Framework for QoE-Aware Intelligent EN-DC Activation

In emerging 5G networks, User Equipment camps traditionally on 4G network. Later, if the user requests a 5G service, it can simultaneously camp on 4G and 5G using EUTRAN New-Radio Dual-Connectivity (EN-DC) approach. In EN-DC, poor radio-conditions in either 4G or 5G network can be detrimental to user Quality-of-Experience (QoE). Although operators want to maximize EN-DC activation to fully utilize the 5G network, sub-optimal parameter configuration to turn on ENDC can compromise key-performance-indicators due to excessive radio-link-failures (RLFs) or voice-muting. While the need to maximize the EN-DC activation is obvious for maximizing the 5G network's utility, RLF and mute avoidance are vital to maintain the QoE requirements. To achieve aforementioned tradeoff, this paper presents the first solution to optimally configure the EN-DC activation parameters. We collect two datasets from real network to develop machine-learning-models to predict RLF and muting, respectively. We also investigate and compare the potential of various under-sampling, oversampling, and synthetic data generation techniques including Tomek-Links and Generative Adversarial Networks for their potential to address the data imbalance problem inherent in the real network training data. Leveraging these models, we formulate and solve two QoE-aware optimization problems that can maximize EN-DC activation while minimizing RLF or voice-muting. System-level simulation-based results show that compared to state-of-the-art solution that does not take into account RLF or voice-muting risk in EN-DC activation, the proposed solution can intelligently determine ENDC activation criteria that minimize the risk of RLF and voice muting while giving the operator's desired level of priority to maximize 5G network utilization.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.

An Intent-driven DaaS Management Framework to Enhance User Quality of Experience

Desktop as a Service (DaaS) has become widely used by enterprises. In 2020, the use of DaaS increased dramatically due to the demand to work remotely from home during the COVID-19 pandemic. The DaaS market is expected to continue growing rapidly [ 1 ]. The quality of experience (QoE) of a DaaS service has been one of the main factors to enhance DaaS user satisfaction. To ensure user QoE, the amount of cloud computation resources for a DaaS service must be appropriately designed. We propose an Intent-driven DaaS Management (IDM) framework to autonomously determine the cloud-resource-amount configurations for a given DaaS QoE requirement. IDM enables autonomous resource design by abstracting the knowledge about the dependency between DaaS workload, resource configuration, and performance from previous DaaS performance log data. To ensure the IDM framework's applicability to actual DaaS services, we analyzed five main challenges in applying the IDM framework to actual DaaS services: identifying the resource-design objective, quantifying DaaS QoE, addressing low log data availability, designing performance-inference models, and addressing low resource variations in the log data. We addressed these challenges through detailed designing of IDM modules. The effectiveness of the IDM framework was assessed from the aspects of DaaS performance-inference precision, DaaS resource design, and time and human-resource cost reduction.

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications.

With the advancement in next-generation communication technologies, the so-called Tactile Internet is getting more attention due to its smart applications, such as haptic-enabled teleoperation systems. The stringent requirements such as delay, jitter, and packet loss of these delay-sensitive and loss-intolerant applications make it more challenging to ensure the Quality of Service (QoS) and Quality of Experience (QoE). In this regard, different haptic codec and control schemes were proposed for QoS and QoE provisioning in the Tactile Internet. However, they maximize the QoE while degrading the system’s stability under varying delays and high packet rates. In this paper, we present a reinforcement learning-based Intelligent Tactile Edge (ITE) framework to ensure both transparency and stability of teleoperation systems with high packet rates and variable time delay communication networks. The proposed ITE first estimates the network challenges, including communication delay, jitter, and packet loss, and then utilizes a Q-learning algorithm to select the optimal haptic codec scheme to reduce network load. The proposed framework aims to explore the optimal relationship between QoS and QoE parameters and make the tradeoff between stability and transparency during teleoperations. The simulation result indicates that the proposed strategy chooses the optimal scheme under different network impairments corresponding to the congestion level in the communication network while improving the QoS and maximizing the QoE. The end-to-end performance of throughput (1.5 Mbps) and average RTT (70 ms) during haptic communication is achieved with a learning rate and discounted factor value of 0.5 and 0.8, respectively. The results indicate that the communication system can successfully achieve the QoS and QoE requirements by employing the proposed ITE framework.

Non-cooperative game algorithms for computation offloading in mobile edge computing environments

Mobile Edge Computing (MEC) has become a promising technology for 5G networks. Computation offloading is an essential issue of MEC, which enables mobile User Equipment (UE) to enjoy rich wireless resources and huge computing power anywhere. This paper considers the Quality-of-Experience (QoE) of UEs in 5G MEC systems and presents a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm for computation offloading of MEC applications. We establish an MEC computation offloading model by considering the QoE requirements of UEs, and discuss the communication overheads, computation cost, and energy consumption models to minimize the energy consumption and time delay of each UE. Considering that there are multiple UEs who want to offload their computation tasks to a resource-constrained MEC server, and each UE is selfish and competitive, we formulate the problem of computation offloading decision as a non-cooperative game model. We prove the existence of a Nash Equilibrium (NE) solution for the proposed game model. In addition, we propose an algorithm that jointly optimizes energy consumption and time delay under QoE preferences to achieve optimal offloading benefits for each UE. Moreover, we respectively propose a dynamic non-cooperative game (QCOG-DG) algorithm and a static non-cooperative game (QCOG-SG) algorithm to efficiently find the NE solution. Extensive simulation experiments are conducted to verify the effectiveness of the proposed MEC computation offloading model and the QCOG-DG and QCOG-SG algorithms. Simulation results show that the proposed QCOG-DG algorithm can efficiently find the NE solutions in the MEC scenarios with UEs of different sizes.

A Multi-Class Channel Access Scheme for Cognitive Edge Computing-Based Internet of Things Networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.

Can Terahertz Provide High-Rate Reliable Low-Latency Communications for Wireless VR?

Wireless virtual reality (VR), a key 3GPP use case of emerging cellular systems, imposes new visual and haptic requirements directly linked to the Quality of Experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low-latency communications (HR2LLC), unlike the low rates commonly associated with ultrareliable low-latency communication. The high rates for VR over short distances can only be supported by an enormous bandwidth, available in the terahertz (THz)-frequency bands. To explore the potential of THz for meeting HR2LLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the Tail-Value-at-Risk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. In particular, the probability distribution function of the THz transmission delay is derived and then used to infer the system reliability scenarios with guaranteed Line of Sight (LoS) as a function of THz network parameters. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HR2LLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99.999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18.3 Gbps can be achieved by the THz network, compared to a reliability of 96% for twice the bandwidth, when blockages are considered.

Big Data Analytics for Mobility Prediction and Its Classification

Kern forecasting is the detection of which customers may leave a service or cancel a subscription for a service. This is an important forecast for many businesses because gaining new customers will cost more than retaining existing customers. The Retail Banking Seer Forecast is an AI-based model that helps customers assess the likelihood of your bank being blocked. Odor is a good indicator of growth. By comparing and analyzing these two metrics that monitor lost customers, and growth rates, new customers, Kern rates accurately tell you how much your business is growing over time. If growth is greater than recession, you can say that your business is growing. With increasing skill requirements and requirements for quality of experience, mobility forecasting has become widely used for mobile communication and has become one of the key processors that use historical transport information to predict the future locations of traffic users. Predictive maintenance refers to the use of data-driven, efficient maintenance methods, via designed to analyze the Status and maintenance of equipment When Predict what needs to be done. Forecasting maintenance is a type of maintenance that directly monitors the health, condition and performance of an asset in real time. Predictive maintenance is aimed at minimizing costly and unexpected breakdowns and gives the manufacturer the opportunity to plan maintenance around their own production schedule. Some examples of the use of forecast maintenance and forecast maintenance sensors include vibration analysis, oil analysis, Includes thermal imaging and equipment monitoring. This approach guarantees cost of savings in routine or time-based preventive maintenance, due to the tasks are only done when guaranteed. Predictive maintenance techniques to determine the condition of equipment in service Designed to help and evaluate when maintenance should be done. Big Data Analytics is structured, semi-structured, Is the use of advanced analytical techniques against very large, diverse datasets. and an unstructured data of various sizes ranging from different sources to terabytes to zeta bytes. Choosing a career in the field of big data and Analytics can be an exciting career endeavor, and it can be the type of role you are trying to find. Machine learning (ML) is a prediction that considers large-scale multidimensional data from a variety of sources allows you to create models. Several studies have been conducted on the use of ML algorithms to predict road traffic. Traffic forecast is floating car data and traffic flow, average traffic speed and based on historical traffic data such as traffic events The task is to predict real-time traffic information.

Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks

Video streaming has become extremely widespread, especially with the growing number of users and the spread of mobile devices, along with the increase in the availability and diversity of multimedia applications and communication technologies. Real-time video communication requires awareness of the quality of experience (QoE) to provide customers with a satisfactory service, for example, in smart cities that use video surveillance systems. The quality of service (QoS) is dependent on network performance, which directly affects the QoE. However, reliance on traditional network infrastructure and routing protocols cannot assure QoS. The emergence of software defined networks (SDN) may eliminate current network limitations. Due to SDN’s global view and programmability characteristics, such capabilities could help in providing an automated QoS control and management. This paper introduces video streaming adaptive QoS-based routing and resource reservation (VQoSRR), which gives SDN networks the ability to meet video demands and enhance user experience over best effort networks, such as the ones required for video surveillance in smart cities. In order to implement QoS-based routing (QBR), we developed algorithms for calculating routing, installing routing paths in the forwarding devices, and shifting traffic to an alternative path when QoE is violated. As well, we used queuing mechanisms to allocate resources based on the QoE requirements of video streaming. Our results indicate that resource reservation mechanisms combined with QoS-based routing enable effective control over routes and resources. Our framework guarantees the video quality as well. This technique of using video streaming would improve the tools and applications used for smart cities such as surveillance systems for hospitals and civil defense organizations.

An efficient virtualized network function deployment scheme for service function chain using deep Q‐network

SummaryVirtualized network functions (VNFs) are virtualized network services running on the network functions virtualization infrastructure in order to replace physical hardware. Integrating with software defined networking and network functions virtualization, internet service providers (ISPs) can chain VNFs together, called service function chain (SFC), to provide various network service demands with virtual links. Many studies have proposed for the VNFs deployment under different objectives, for example, energy, delay, quality of service (QoS), and quality of experience (QoE). From the aspects of both ISPs and customers, both energy consumption and QoE are the primary concerns for the purpose of operating expense reduction and users' satisfaction. This paper proposes a QoS/QoE/energy‐aware deep Q‐network (DQN)‐based scheme, called DQN‐QQE for VNFs deployment under the consideration of both energy consumption and QoE requirement with QoS constraints. The reward of the proposed scheme is formulated by the Weber‐Fechner law and the exponential interdependency of QoE and QoS (IQX) hypothesis. Afterwards, we compared the proposed DQN‐QQE to the QoS/QoE‐aware approach DQN‐Q2‐SFC, brute force, and randomness in terms of energy, QoE, error rate, and processing time. The simulation results revealed that DQN‐QQE is more stable and is better than DQN‐Q2‐SFC and randomness approach in terms of energy, QoE, and error rate. The average processing time and energy consumption of the proposed DQN‐QQE was 43% and 11% less than DQN‐Q2‐SFC, respectively. Although brute force approach is better than others in terms of energy, QoE, and error rate, the processing time of brute force approach is nearly 20 times larger than the proposed DQN‐QQE.

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

Energy-efficient Radio Resource Management (RRM) for 5G and beyond networks has become a key research challenge due to increasing Small Cells (SCs) densities and the high Quality of Experience (QoE) requirements of business users. Ensuring QoE and energy efficiency is essential in mobile networks, but these goals are often opposing and rarely addressed simultaneously in existing solutions. In this paper, we propose to include the QoE criterion in the RRM technique for 5G and beyond multi-layer networks, which will allow ordering individual QoE for business users. We developed a new radio resource allocation and optimization method to address changing user QoE requirements and reduce energy consumption in multi-layer 5G networks. The proposed method differs from the known ones in that it considers the QoE requirements of business users and load localization to optimally distribute the service process between Macro Cells (MCs) and SCs. This method uses a Voronoi diagram to energy-efficiently design the 5G Radio Access Network (RAN) by switching SCs to sleep mode when they are not serving active users. As a result, a balance is struck between user QoE requirements and network energy efficiency. Based on simulations, it is proved that the proposed method allowed more efficient use of accessible radio resources by 25% and reduced the energy consumption of the 5G RAN by 8.7% to provide the ordered QoE for users compared to traditional RRM methods.

Cloud-based XR Services: A Survey on Relevant Challenges and Enabling Technologies

In recent years, the emergence of XR (eXtended Reality) applications, including Holography, Augmented, Virtual and Mixed Reality, has resulted in the creation of rather demanding requirements for Quality of Experience (QoE) and Quality of Service (QoS). In order to cope with requirements such as ultra-low latency and increased bandwidth, it is of paramount importance to leverage certain technological paradigms. The purpose of this paper is to identify these QoE and QoS requirements and then to provide an extensive survey on technologies that are able to facilitate the rather demanding requirements of Cloud-based XR Services. To that end, a wide range of enabling technologies are explored. These technologies include e.g. the ETSI (European Telecommunications Standards Institute) Multi-Access Edge Computing (MEC), Edge Storage, the ETSI Management and Orchestration (MANO), the ETSI Zero touch network & Service Management (ZSM), Deterministic Networking, the 3GPP (3rd Generation Partnership Project) Media Streaming, MPEG’s (Moving Picture Experts Group) Mixed and Augmented Reality standard, the Omnidirectional MediA Format (OMAF), ETSI’s Augmented Reality Framework etc.

H.264 and H.265 video traffic modeling using neural networks

As video has become the dominant type of traffic over wired and wireless networks, the efficient transmission of video streams is of paramount importance. Hence, especially for wireless networks, the optimum utilization of the available bandwidth while preserving the users’ Quality of Service and Quality of Experience requirements is crucial. Towards this goal, the accurate prediction of upcoming video frame sizes can play a significant role. This work focuses on achieving such an accurate prediction for videos encoded with H.264 and H.265, which are the major state-of-the-art standards based on their current market share. Unlike previous studies, we use single-step and multi-step approaches to capture the long-range dependence and short-range dependence properties of variable bit rate video traces through neural networks-based modeling. We evaluate the accuracy of Long Short Term Memory, Convolutional Neural Networks and Sequence-to-Sequence models and compare them with existing approaches. Our models show significantly higher accuracy for a variety of videos. We also provide a case study on how our model can be used for traffic policing purposes.