5G Radio Access Network (RAN) slicing provides support for resource isolation and dynamic resource optimization to accommodate the diverse Quality of Service (QoS) needs across Internet of Things (IoT) ecosystems, i.e., enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communication (mMTC) or Best Effort (BE) services. However, existing optimization- and data-driven radio resource allocation (RRA) models often suffer from rigid/suboptimal resource distribution and increased Service Level Agreement (SLA) violations in dynamic, multi-service environments. This paper introduces an Adaptive QoS-aware Priority Scheduling (AQPS) solution, a novel RRA approach designed to optimize resource distribution for multi-service 5G network slicing deployments in IoT-enabled infrastructures. To optimally allocate resource block groups (RBGs) according to time-varying traffic patterns and IoT network conditions, we formulate an NP-hard optimization problem with the objective of minimizing SLA violations while satisfying QoS requirements for diverse IoT service classes. In this context, AQPS optimizes resource distribution for IoT services by incorporating: (i) minimum guarantee allocation (QoS-driven RBG estimation), (ii) weighted urgency-based resource distribution (computing user urgency by incorporating buffer state, QoS factors, spectral efficiency, and service priorities), and (iii) priority-based round-robin allocation. Extensive trace-driven simulation findings from two comprehensive multiuser IoT-dense urban and enhanced IoT coverage zone scenarios with realistic channel conditions demonstrate that the AQPS solution experiences a very low number of SLA violations while enabling SLA assurance rates of 99.07% and 97.58%, respectively, when compared against state-of-the-art benchmarks, including Deep Reinforcement Learning (DRL), Round Robin (RR), and Stepwise Optimal Algorithm (SOA).

Software-Defined Networking (SDN) has emerged as a key enabler for intelligent vehicular edge computing by providing centralized control and adaptive resource management. However, achieving reliable controller placement under Byzantine Fault Tolerance (BFT) and efficient task offloading in highly dynamic vehicular environments remains a major challenge due to frequent topology changes, communication delays, and control decision overhead. Existing works treat controller placement and task offloading as independent optimization problems and do not account for reassignment frequency, controller decision time, or BFT-compliant controller mapping. To address these limitations, this paper proposes MoTO-BFT , a novel M ulti- o bjective T ask O ffloading and B yzantine F ault- T olerant controller placement framework for SDN-enabled vehicular networks. MoTO-BFT integrates Temporal Convolutional Network (TCN)-based trajectory prediction with a novel Fast-converging Multi-objective Gradient Aggregation algorithm (FaMOGA) to jointly optimize BFT-compliant controller placement and computation task offloading. The proposed framework minimizes communication and computation delay, controller decision time, controller count, and reassignment frequency while ensuring balanced load distribution and 3 f + 1 BFT controller mapping. Extensive simulations using real vehicular mobility traces demonstrate that MoTO-BFT significantly outperforms existing controller placement and offloading baselines in terms of latency reduction, reassignment stability, and BFT robustness, making it a highly efficient and reliable solution for dynamic vehicular SDN environments.

Multi-sensory Extended Reality (mXR) applications represent a cornerstone technology for immersive communications in the 6G and IMT-2030 frameworks, enabling synchronized stimulation across multiple human sensory modalities to create authentic immersive experiences. mXR applications demand simultaneous satisfaction of stringent data rate, delay, and reliability requirements across all sensory streams, necessitating an unprecedented convergence of enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) service paradigms. Existing 5G scheduling schemes, designed to optimize either eMBB or URLLC services independently and typically handling decoupled data streams, do not address the cross-modal synchronization and joint optimization challenges of mXR applications. This paper proposesDREAM-X (Delay-Reliability Aware Scheduling for Multi-sensory XR Applications), a unified scheduling framework specifically designed for next generation (NextG) networks to support mXR services. This framework includes a novel Multi-modal Delay Tracking Queue architecture that organizes multi-sensory data units based on transmission deadlines, enabling precise cross-modal delay coordination. We formulate the scheduling problem as a constrained optimization and develop a Model Predictive Control (MPC) solution with rolling horizon optimization to ensure computational tractability for real-time deployment. The simulation results show that DREAM-X provides multi-fold gains in the number of satisfied mXR users compared to conventional and state-of-the-art XR scheduling approaches, while maintaining strict delay and reliability constraints.

Sequential federated learning (SFL) trains models collaboratively across clients in a chain manner. This order shows communication efficiency compared to traditional FL in a parallel manner with a star topology. However, SFL can fail to produce stable training results when clients have significant statistical heterogeneity among their local data distributions. To address these challenges, we propose a novel element-wise model decoupling framework namedSeqFedEDTthat accelerates SFL training by separating model parameters of each client into a shared subset for global knowledge collaboration and a personalized subset for migrating data heterogeneity. We explore three types of parameter contribution scoring metrics based on gradient, Fisher information, and parameter importance (PI) for personalized parameter selection. In addition, we propose a quantile-based thresholding mechanism to separate shared and personalized subsets and explore the best performance quantile selection in numerical studies. Extensive experiments demonstrate thatSeqFedEDToutperforms eight state-of-the-art methods across diverse datasets and heterogeneity scenarios. All code and results are available athttps://github.com/tian0920/SeqFedEDT.

Optimal tile-based bitrate allocation improves the Quality of Experience (QoE) for adaptive 360° video streaming across multiple clients in heterogeneous network environments; however, it is challenging as it implies accurate viewport prediction, finest tile-based bitrate reservation, and maintaining QoE fairness, particularly under constrained network conditions. This paper proposes a strategy named EDGE360, that employs an edge-driven Multi-Agent Deep Reinforcement Learning (MADRL) solution for rate adaptation to improve the joint QoE in DASH-based rich media content delivery based on adaptive viewport prediction and Video Multi-method Assessment Fusion (VMAF) corresponding tiling granularity selection. Cooperative strategies among agents in the central critic network are crucial for addressing the complexity of network instances at the edge and optimizing media streaming bitrate assignment in multiple-client scenarios. Therefore, EDGE360 aims to implement the Counterfactual Multi-Agent Policy Gradients (COMA) based on 5G network traces to train agents in policies that optimize individual client QoE and fairness among clients, resulting in an improved rich streaming experience. At the edge, a tile-based quality monitor evaluates viewport trajectories, buffer status, and network throughput, employing deep learning to forecast optimal tile bitrate allocation, which is formulated as an MDP and solved with MADRL. Based on extensive experimentation, EDGE360 surpasses state-of-the-art adaptive bitrate algorithms by achieving the highest average reward, outperforming RAPT360, 360SRL, and BOLA360 by 8.12%, 11.86%, and 18.00%, respectively, demonstrating superior convergence and refinement.

Multiple approaches aim to enhance user experience in the delivery of immersive video content. The popularisation of VR, combined with recent advances in mulsemedia technology has improved access to immersive visual and olfactory stimuli. Synchronising multiple scent dispensers positioned around the user when watching 360° videos can more accurately indicate the location of scent sources, guiding users to move their heads accordingly to the indicated directions. However, the manual annotation process required to add mulsemedia effects is labour-intensive, limiting the availability of content with sensory enhancements, particularly when using multiple scent dispensers from various directions. Addressing this issue, this paper introduces OmniScent-CNN, an innovative solution to automate the diffusion of scents from different directions in a VR environment using Convolutional Neural Networks (CNNs) for scene recognition. Multiple instances of the solution were tested, employing a number of CNN architectures. The results demonstrated that olfaction accuracy can reach up to 71.28% with the ResNet-18 model. Furthermore, user perceptual tests revealed excellent results, with 87.5% of participants agreeing or strongly agreeing that the scents enhanced their enjoyment of the experience. This indicates the feasibility of automating the process of synchronising omnidirectional scents based on 360° scene recognition.

In today’s rapidly evolving telecommunications landscape, the demand for seamless connectivity and top-tier network performance has reached unprecedented levels. Traditional cellular systems, while valiant in their service, now struggle under the weight of spiraling data demands, spectrum scarcity, and power inefficiency. The era of ultra-dense mobile networks, with Heterogeneous Networks (HetNets) at the forefront, ushers in improved throughput, spectral efficiency, and energy management. To tackle these challenges, this paper introduces MLCIMO (Machine Learning-enhanced Classification for Interference Management and Offloading) into 5G HetNets. MLCIMO employs a multi-binary classification strategy to categorize users based on interference types and levels. It also introduces an offloading scheme tailored to user service priorities, enhancing the user quality of experience, while conserving energy. It seamlessly aligns with the evolving needs of the HetNets, addressing some of the issues introduced by small cell deployments. Simulation results show that MLCIMO achieves the highest throughput, shortest delay, and lowest packet loss ratio in comparison with alternative approaches. In a comprehensive analysis, the varying degrees of interference encountered by users under different schemes are unveiled, further establishing MLCIMO’s distinguished position in mitigating interference.

The increasing volume of video traffic presents significant challenges to network transmission, while edge computing accelerates video delivery by leveraging caching and computation to optimize content forwarding. However, as edge computing is generally deployed by service providers in a transparent manner, clients cannot perceive edge states, e.g., cache availability, potentially resulting in suboptimal bitrate decisions. This issue persists even with intelligent bitrate selection approaches on the client side, as the inaccurate estimation of network delivery capacity due to edge cache transparency remains unresolved. Meanwhile, single-edge servers or nodes, with limited cache space and computational capacity for a small number of users, can be more effective by aggregating into clusters to better serve users and optimize resource utilization. Therefore, we propose an edge-assisted bitrate re-adaptation scheme (e-BitRead) for adaptive streaming, utilizing neighbor edges to accelerate video deliveries.e-BitReadintroduces three key innovations: (i) it employs a bitrate re-adaptation mechanism that intelligently selects alternative bitrates from edge servers instead of strictly responding with the requested bitrate, (ii) it utilizes collaborative caching across multiple edge servers to expand available bitrate options through coordinated resource sharing, and (iii) it enhances the learning efficiency through joint optimization of network architecture and reward design, which leverages actor-critic structure to fit into multi-edge bitrate adaptation. In experiments with an intelligent client ABR,e-BitReaddemonstrates its superiority by achieving a 1.43x higher hit ratio compared to the baseline, while improving QoE by 1.93x over the scheme without smart bitrate matching and delivering a 33% gain over the single-edge re-adaptation approach.

The Internet of Vehicles (IoV) represents a significant advancement in intelligent transportation systems (ITS), enabling real-time data exchange between vehicles and infrastructure to enhance road safety, traffic efficiency, and user experiences. Vehicular edge computing (VEC) has emerged as a critical enabler, offering localized data processing and facilitating data trading among vehicles and edge servers. While data trading enhances system efficiency by enabling seamless information exchange among vehicles and infrastructure, it also introduces significant challenges in preserving user privacy, protecting against malicious tracking, and managing energy consumption effectively. To tackle these challenges, we propose a novel Energy-Efficient Pseudonym Management with Dueling Deep Q-Network (E2PM-DDQN) framework for VEC-enabled IoV, aiming to enhance the balance between privacy protection and energy efficiency during data trading. By deploying an RL agent at VEC servers, our approach dynamically manages pseudonym updates during data trading, improving the trade-off between privacy and energy consumption. Simulation results in a VEC-enabled IoV environment confirm that our proposed E2PM-DDQN framework achieves higher privacy entropy than state-of-the-art approach, enables higher rewards than baseline strategies, and ensures lower energy consumption. These results highlight how a reinforcement learning (RL) approach outperforms non-RL methods.

Holographic-Type Communication (HTC) is poised to revolutionize immersive telepresence and extended reality (XR) applications by enabling ultra-realistic, volumetric interactions. However, delivering high-fidelity 3D holographic content over bandwidth-constrained and variable wireless links presents significant challenges due to its inherently high data demands and real-time requirements. This paper proposes a novel utility-aware adaptive streaming framework for segmented holographic video, wherein each frame is decomposed into semantically meaningful components—face, hands, and body pose—encoded at multiple resolution levels using Draco compression. The adaptive selection of segment resolutions is formulated as a 0-1 Knapsack optimization problem, aiming to maximize perceived utility under dynamic bandwidth constraints. Segment utilities are modeled using diverse temporal decay functions—linear, exponential, and logarithmic—to capture differential importance over time. We implement and evaluate the full system in Network Simulator 3.40, integrating realistic network traces and application-level utilities. Experimental results demonstrate significant gains in bandwidth utilization, segment delivery completeness, and overall Quality of Experience (QoE), compared to non-adaptive and static strategies. The proposed approach represents a practical and extensible foundation for real-time holographic streaming in future 5G/6G networks.

Unmanned aerial vehicles (UAV) are widely used for edge computing in poor infrastructure scenarios due to their deployment flexibility and mobility. In UAV-assisted edge computing systems, multiple UAVs can cooperate with the cloud to provide superior computing capability for diverse innovative services. However, many service-related computational tasks may fail due to the unreliability of UAVs and wireless transmission channels. Diverse solutions were proposed, but most of them employ timedriven strategies which introduce unwanted decision waiting delays. To address this problem, this paper focuses on a taskdriven reliability-aware cooperative offloading problem in UAV-assisted edge-enhanced networks. The issue is formulated as an optimization problem which jointly optimizes UAV trajectories, offloading decisions, and transmission power, aiming to maximize the long-term average task success rate. Considering the discrete-continuous hybrid action space of the problem, a dependenceaware latent-space representation algorithm is proposed to represent discrete-continuous hybrid actions. Furthermore, we design a novel deep reinforcement learning scheme by combining the representation algorithm and a twin delayed deep deterministic policy gradient algorithm. We compared our proposed algorithm with four alternative solutions via simulations and a realistic Kubernetes testbed-based setup. The test results show how our scheme outperforms the other methods, ensuring significant improvements in terms of task success rate.

This paper addresses the rate control issue for Video-on-Demand (VoD) services in Low Earth Orbit (LEO) satellite Internet. LEO systems employ long-distance Non-Orthogonal Multiple Access (NOMA), where the transmission rate of the last hop directly determines the Quality of Experience (QoE) levels for the VoD users and the satellite’s energy consumption. Our research identifies two primary issues: (i) determining the transmission rate to ensure high user QoE while minimizing energy consumption, and (ii) ensuring fairness among users within the satellite coverage area. To address these issues, we model the multi-user VoD viewing process as a Partially Observable Markov Process (POMDP) and describe the interactions among users using a cooperative coalition game framework. We propose Harmony, a distributed and dynamic improvement solution based on the Deep Deterministic Policy Gradient (DDPG) approach. Harmony intelligently determines each user’s transmission rate by combining feedback from user applications and MEC server metrics, ensuring superior QoE levels, energy efficiency, and fairness. The trained Harmony can be adapted to various Adaptive BitRate (ABR) algorithms, providing scalability and immediate applicability in existing LEO networks. It can also achieve improved performance in dynamic user environments. Simulation results demonstrate that Harmony improves energy efficiency and fairness, while maintaining high QoE levels and reducing MEC traffic overhead by 28.1% to 62.6%.

As one of the latest features of ultra-high-definition media services, high frame rate can significantly enhance perceptual quality, but also increases codec complexity in the transmission chain, leading to additional overhead. In this paper, we carry out comprehensive offline experiments in which the codec overhead (e.g., energy and delay) shows a linear or even quadratic increase trend with various frame rates, while correspondingly, when the frame rate increases to 75FPS, its bitrate is 24.2% lower than that under 15FPS for several scenarios. This illustrates that the overhead is more significant than the load from data traffic in the frame rate control problem. Thus, we propose a Bilateral Adaptive video Transmission framework that establishes Bilateral game-theoretic Control (BAT-BC) between sender and viewer. Through dynamically adjusting frame rate for sender and service payment for viewer, BAT-BC can flexibly adapt to the external environment such as computational state and scenario changes and it is expected to provide viewers with a smoother experience. Furthermore, we extend it to the scenario including concurrent multi-viewer and discuss the effects of grouping utility. Finally, we design a prototype system and the proposed solution is deployed on it to evaluate the performance. The frame drop rate is reduced by 61%, resulting in a 31% improvement in subjective QoE. The objective metric achieves the same level of actual experience as a fixed 60 FPS under dynamic environment.

The evolution of vehicular networks from vehicular ad-hoc networks (VANETs) to Internet of Vehicles (IoVs) has played a pivotal role in the intelligent transportation system (ITS). However, these networks are increasingly vulnerable to security, privacy, and trust (SPT) threats due to various emerging attacks. In response, Reinforcement Learning (RL) has emerged as a promising technique for strengthening vehicular network security. This paper provides a comprehensive exploration of the SPT challenges within vehicular networks and presents RL as a promising solution for enhancing SPT provisioning. First, we provide a tutorial on vehicular networks and integrated concepts, and the overview of RL concepts and different types of RL. Then, we conduct a detailed analysis of existing RL-based solutions, categorizing them within two novel taxonomies: one based on the specific SPT focused area and the other on the specific RL methods employed. We conclude by discussing key lessons learnt, current open challenges, and potential future directions in this rapidly evolving field.

When deploying software defined network (SDN), there is a need for optimized controller placement to ensure high network performance. Different solutions are proposed that focus on single optimization objectives, such as delay minimization or throughput maximization. This article proposes a hybrid artificial intelligence-based approach, the reliability–capacity aware joint controller placement and routing (ReCAP), which combines deep reinforcement learning and graph neural networks to identify the best placement for controllers, so there are optimal paths for communication between switches and controllers and improved network performance. Controller placement is optimized based on multiple objectives, including load balancing, path reliability and link capacity. ReCAP is validated using real network traces and evaluated using the Panopticon network. The experimental results show an improved performance for our ReCAP approach compared to existing solutions in terms of both efficiency and reliability.

Computation offloading is an effective method for reducing the pressure put on networks and improving the service experience. However, most existing research on computation offloading is timeslot-driven and treats all tasks equally, resulting in decision waiting delays and failure to complete some important tasks. In this paper, we propose a novel priority-aware task-driven computation offloading model with system performance gain as the optimization objective based on a combination of task delay and energy consumption aspects. The new model is formulated as a Markov decision process (MDP). Considering the discrete-continuous hybrid action space of the optimization problem, we construct a dependence-aware latent space and propose a novel algorithm based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). Additionally, we present the neural network structure and analyze the complexity of the algorithm. Extensive simulations show how our algorithm achieves superior performance compared to three state-of-the-art alternative approaches.

Permissioned distributed ledgers (PDLs) provide security and trust for Internet of Vehicles (IoV) applications, but face scalability issues due to resource-intensive consensus mechanisms. To address this, we propose a novel hybrid consensus network (HCN) architecture that leverages the computational capabilities of parked connected autonomous vehicles (CAVs) through a multi-layer vehicular edge computing (VEC) framework. The HCN is designed following guidelines outlined by the European Telecommunications Standards Institute (ETSI) regarding the structuring of PDLs. It aims to improve the performance, reliability and scalability of PDL-based IoV networks while maintaining their security and trust guarantees.

The ever-growing demand for video and data services poses a significant challenge for mobile network operators. This surge is fueled by the proliferation of high-definition applications, online gaming, and the rise of machine-type communications' entailing exceptional Quality of Experience (QoE) for users. To address these demands, Heterogeneous Networks (HetNets) have emerged as a promising solution within 5G networks. However, the coexistence of diverse small cells in multi-tier 5GHetNets introduces complex interference issues. This paper introduces a novel Machine Learning Enhanced Classification for Interference Management and Offloading (MLCIMO) scheme. It employs multi-binary classification to categorize users by interference types and levels, improving QoE for various services and enhancing resource utilization. A comprehensive performance comparison with state-of-the-art approaches was also conducted to showcase the advantages of MLCIMO in terms of Video Multimethod Assessment Fusion (VMAF), R-Factor, and RUM Speed Index (RUMSI) metrics.

To reduce costs and improve performance, video Content Delivery Networks (CDNs) have started to incorporate lightweight edge nodes, e.g., WiFi access points. Because of this, it is necessary for CDNs to intelligently select which video files should be placed at their core data centers vs. these edge nodes. This is more complex than traditional CDN management, as lightweight edge nodes are much more numerous and unstable than data centers. With this in mind, we present SDPush —- a system for managing content placement in edge CDNs. SDPush tackles two problems. First, it is necessary for SDPush to select which files to proactive push. To address this, we build a file popularity prediction model that effectively identifies video files that will receive many views. Second, SDPush should determine how many replicas of each file to push. To address this, we design a model to predict the benefits of pushing particular files (regarding traffic savings) and then formulate the replica decision problem as a lightweight problem, which is solvable within seconds, even for platforms that accommodate millions of daily active users. Through a trace-driven evaluation and a live deployment on a real video platform, we validate SDPush’s effectiveness, offloading peak-period traffic by 12.1% to 23.9% from the data center to edge nodes, thereby reducing the CDN costs.

This paper introduces the Utility-Based Multipath Transmission Control Protocol (uMPTCP), an innovative extension of MPTCP designed for prioritized extended reality (XR) content delivery, based on monitored Quality of Service (QoS) network metrics. The proposed approach includes an algorithm that assesses subflows’ delivery performance and dynamically selects the most efficient one to deliver prioritized content with reduced latency. A comparison is made against a stateof-the-art solution, the virtual private channel (VPC), and the default MPTCP algorithm. The evaluation considers both singlehomed and multi-homed configurations in scenarios with varied bandwidth requirements, including XR. The study is conducted within the framework of the FRADIS project, aimed at providing a comprehensive solution for 5G and beyond heterogeneous network environments. FRADIS integrates machine learning to optimize service-specific approaches, allowing a choice between traffic engineering with network slicing and protocol-based solutions, including the proposed uMPTCP solution. The framework supports a diverse range of services for smart city monitoring, XR applications, e-health solutions and entertainment.

The emergence of 5G networks signifies a shift towards a range of unique and high-demand network services, which prioritize conflicting Quality of Service (QoS) requirements for diverse applications such as video streaming, Virtual Reality (VR), and gaming services. Network slicing in 5G architecture is crucial in addressing these diverse needs using a common physical infrastructure. The integration of 5G New Radio (NR) introduces promising features such as Bandwidth Parts (BWPs), facilitating flexible resource configuration for each User Equipment (UE) within dedicated bandwidth subsets. However, as network slicing continues to mature, the effective implementation and management of Radio Access Network (RAN) slices for these multifaceted services remains a considerable hindrance. This paper introduces an innovative 5G Slice Streaming solution (5GSS) specifically tailored to elevate content streaming performance and address the unique requirements of various slices of different content types e.g. adaptive video (DASH), VR, and gaming. With a primary focus on video streaming and considering it as one of the pivotal slices, the proposed 5GSS solution simultaneously considers multiple parameters such as buffer impact factor, download impact factor, throughput proximity, and number of quality switches, and applies a Pareto optimization to identify and perform the optimal bitrate selection in a 5G RAN slicing environment. The comprehensive NS3 experimental setup accommodates 5G NR and DASH modules. The experimental results validate the superior performance of the proposed solution, demonstrating noticeable improvements in visual quality, quality smoothness, throughput efficiency and fairness, compared to existing solutions.

The stringent requirements of the latest immersive applications (i.e., 360° video, VR, AR, XR, etc.) combined with the scarcity of radio resources are challenging the service operators of the various telecommunication networks. In the recent literature, HetNets have been proposed as an alternative to overcoming these challenges. In previous work, the authors presented SVORA, an architecture for integrating different radio technologies, combining virtualized/open RAN architectures and SBA-based RAN for efficient spectrum usage. In this work, the authors upgrade the previously proposed solution to deliver immersive video efficiently. The paper presents imeNF, a new set of Network Functions that support delivering high-bitrate immersive video-based applications with increased quality of service. Simulations-based testing shows how, based on imeNF, a 16k video can be sent with delays of 15 ms, while traditional D-RAN, V-RAN, and O-RAN-based approaches take more than 300 ms.

Vehicular edge computing (VEC) empowers real-time applications in the autonomous vehicle (AV) domain by positioning edge servers closer to AVs. This proximity reduces latency and energy consumption for task processing. However, effectively managing the offloading of these tasks and allocating resources across dynamic vehicular environments poses significant challenges. Centralized strategies face scalability hurdles, while decentralized approaches often lack cooperative and co-ordinated mechanisms. To tackle these limitations, this paper introduces a novel decentralized framework for optimizing task management and network resource utilization in dynamic vehicular settings. This framework is equipped with a multi-agent deep reinforcement learning algorithm (MADRL) that makes intelligent task offloading decisions. The proposed algorithm considers the diverse computing capabilities of network entities and enhances energy efficiency without compromising latency or task completion rates. The simulation-based performance assessment demonstrates the effectiveness of this framework in reducing energy consumption and improving task completion rates in comparison to existing algorithms.

In recent years, with the rapid development of mobile network communication and robotic vehicle technolo-gies, there has been a growing emphasis on the design and development of sustainable intelligent robotic vehicle systems. To address this trend, we propose CoSPAM, an innovative multi-robot collaboration for simultaneous path planning and semantic mapping in a large-scale environment. CoSPAM integrates local and global strategies to optimize planning and mapping processes. Each robot operates autonomously and executes local rapidly exploring random tree exploration within its designated spatial environment. The robots establish wireless communication to transmit local map information to the server. We employ Bayesian-based fusion and Bundle Adjustment-based optimization techniques in the server to construct a comprehensive global 3D semantic OctoMap. Based on spatial memory mechanisms and time constraints, we utilize knowledge from all robots to formulate a cohesive global path plan. The experimental results demonstrate that CoSPAM reduces exploration time by almost 56.3% compared to traditional methods. It optimally allocates each robot's exploration goals, minimizing the exploration path's length. Moreover, CoSPamdemonstrates its capability to gen-erate a high-quality 3D semantic OctoMap. CoSPAM offers a reliable, agile, and energy-efficient solution for large-scale environment planning and mapping among sustainable intelligent robotic vehicles.

The development of the Internet of Vehicles (IoV) and autonomous vehicles plays a significant role in intelligent transportation systems (ITS) that are empowered by vehicular networks. However, the dynamic nature of these networks presents challenges that need to be addressed. Reinforcement learning (RL) has emerged as an effective technique for strengthening vehicular networks. The use of standard single-agent RL and deep reinforcement learning (DRL) has recently been demonstrated to enable each network entity as a decision-making agent to adapt to unknown environments by learning an optimal decision-making policy. However, in the complex and dynamic environments of vehicular networks, the limitations of single-agent approaches become apparent. Multi-agent reinforcement learning (MARL) offers a compelling alternative, enabling net-work entities to learn their optimal policies by observing the environment as well as the policies of other network entities. Due to this, MARL has recently been used to solve various problems in IoV by improving its learning efficiency. In this paper, we review the applications of MARL in IoV networks. Following the review, four main application areas for MARL in IoV were identified, namely: resource management, task offloading, trust management, and privacy preservation. Furthermore, the MARL-based approaches in IoV were classified into three main categories: fully centralized, fully decentralized, and centralized training with decentralized execution (CTDE) depending on the MARL architecture employed. Finally, we discuss the challenges, open issues, and future directions related to the applications of MARL in the IoV.

In the interest of addressing mobile users' Quality of Experience (QoE) demands and ensuring good Quality of Service (QoS) for innovative, high-performing services, the forthcoming generation of wireless networks is integrating Multi-access Edge Computing (MEC), Software Defined Mobile Networks (SDMN), and Cloud Radio Access Networks (C-RAN). These technologies aim to enhance performance and assure QoS in light of the growing complexity of telecom networks. They also aim to address escalating traffic and user demands for higher bitrate speeds. This paper explores resource allocation across a wireless network empowered by MEC, SDMN, and C-RAN technologies to facilitate high-quality adaptive video streams. We introduce a MEC server collaboration-based Cross-Layer Bitrate Allocation algorithm that leverages user and RAN MAC layer data, including Reference Signal Received Power (RSRP), traffic behaviors, and preferred video quality, to optimize users' QoE while minimizing backhaul traffic by reducing caching requests from the Central Cloud, located in operator backhaul. Addressing a mixed-integer nonlinear programming challenge, we consider radio resource availability constraints and MEC servers' storage and transcoding capacities of MEC servers. The proposed algorithm, termed Cross-Layer MEC-Enabled Bitrate Allocation (CLMEBA), aims to enhance users' QoE by minimizing the discrepancy between the achievable throughput at the MAC layer and the allocated bit rate for video frames at the application layer while also reducing backhaul traffic through MEC server collaboration. Compared with a baseline scheme, our algorithm realizes a 22.36 % enhancement in system utilization rate, a 18.11 % improvement in video quality, and a 49.87% reduction in backhaul traffic.

With the rapid popularity of short video applications, a large number of short video transmissions occupy the bandwidth, placing a heavy load on the Internet. Due to the extensive number of short videos and the predominant service for mobile users, traditional approaches (e.g., CDN delivery, edge caching) struggle to achieve the expected performance, leading to a significant number of redundant transmissions. In order to reduce the amount of traffic, we design a Novel Coded Transmission Mechanism (NCTM), which transmits XOR-coded data instead of the original video content. NCTM caches the short videos that users have already watched in user devices, and encodes, multicasts, and decodes XOR-coded files separately at the server, edge nodes, and clients, with the assistance of cached content. This approach enables NCTM to deliver more short video data given the limited bandwidth. Our extensive trace-driven simulations show how NCTM reduces network load by 3.02%-14.75%, cuts peak traffic by 23.01%, and decreases rebuffering events by 43%-85% in comparison to a CDN-supported scheme and a naive edge caching scheme. Additionally, NCTM also increases the user's buffered video duration by 1.21x-13.53x, ensuring improved playback smoothness.

As a key enabling technology in intelligent heterogeneous Internet of Things (IoT), edge caching provides important support for reducing core network load and improving network service efficiency, especially for high bandwidth demand services represented by multimedia applications. However, external time-varying information is hard to be obtained comprehensively in a complicated heterogeneous IoT environment. Meanwhile, there exists the substitutability of content (e.g., videos with different bitrates), which is difficult to make caching decisions online in real-time to achieve fast feedback with low latency and avoid useless deployment. To this end, this article designs a transcoding-enabled online cache scheme for IoT video service with cloud–edge–terminal collaboration. First, we design a variable bitrate video routing strategy to dynamically retrieve content from cloud/edge according to user demands. Furthermore, the video caching problem is considered as an online convex optimization problem to learn utility gradient and determine the optimal caching strategy in real-time without any prior information. On this basis, we extend the problem to elastic networks with dynamic available resources and prove the sublinear regret and sublinear constraint violation. Finally, we summarized five video request data sets and carried out differentiated multiple verifications based on different request habits and content requirements. Compared with the most advanced algorithms in terms of delay, we evaluated the performance advantages of the proposed scheme.

Multi-access Edge Computing (MEC) has revolutionized the delivery of large-scale mobile multimedia services by endowing network edge with computing and caching capabilities. This not only relieves the load on core networks, but also significantly reduces data access latency. However, deploying edge data centers with a high density to accommodate the growing demand for multimedia services is not cost-effective. With the rapid development of the Internet of Things (IoT) industry, recent studies have shown that by allowing UAVs with integrated computing and communication to form a Mobile Device Cloud (MDC) environment via UAV-to-UAV (U2U) communications in IoT networks, UAVs can play an important role in assisting cellular networks with multimedia delivery and providing excellent service for IoT devices on the ground. While a MDC environment composed of UAVs offers flexibility and cost-effectiveness, the challenge remains in allocating caching resources in a timely manner to meet the dynamic content demands. To address this challenge, we design a novel Mobile Device Cloud-enabled Caching (MDC) framework, which makes use of the available caching and U2U communication capabilities to enable any UAV to obtain dynamically content from other nearby UAVs via the IoT network. By modeling the dynamic network status as a fluid-based system, MDC employs a dynamic caching allocation algorithm to minimize both service latency and caching costs. Extensive experiments demonstrate that MDC outperforms a state-of-the-art MDC multimedia delivery approach by improving average cache utilization with over 40% and reducing average access latency with more than 25%.

Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delays. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discrete-continuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms three state-of-the-art alternative solutions in terms of task delay and system gain.

Next-generation cellular networks strive to offer ubiquitous connectivity, enhanced transmission rates with increased capacity, and superior network coverage. However, they face significant challenges due to the growing demand for multimedia services across diverse devices. Adaptive multimedia streaming services are essential for achieving good viewer Quality of Experience (QoE) levels amidst these challenges. Yet, the existing adaptive video streaming solutions do not consider diverse QoE preferences or are limited to meeting specific QoE objectives. This paper presents FReD-ViQ, a Fuzzy Reinforcement Learning-Driven Adaptive Streaming Solution for Improved Video QoE that combines the strengths of fuzzy logic and advanced Deep Reinforcement Learning (DRL) mechanisms to deliver exceptional, individually tailored user experiences. FReD-ViQ is a sophisticated streaming solution that leverages efficient membership function modelling to achieve a more finely-grained representation of both input and output spaces. This advanced representation is augmented by a set of fuzzy rules that govern the decision-making process. In addition to its fuzzy logic capabilities, FReD-ViQ incorporates a novel DRL algorithm based on Dueling Double Deep Q-Network (Dueling DDQN), noisy networks, and prioritized experience replay (PER) techniques. This innovative fusion enables effective modelling of uncertain network dynamics and high-dimensional state spaces while optimizing exploration-exploitation trade-offs in adaptive streaming environments. Extensive performance evaluations in real-world simulation settings demonstrate that FReD-ViQ effectively surpasses existing solutions across multiple QoE models, yielding average improvements of 23.10% (Linear QoE), 23.97% (Log QoE), and 33.42% (HD QoE).

The Software-Defined Mobile Network (SDMN), Multi-Access Edge Computing (MEC), Cloud RAN (C-RAN), and Network Slicing are the promising solutions that have been defined for the next generation of the wireless mobile networks in order to fulfill the increasing Quality of Experience (QoE) demand of the mobile users and the Quality of Service (QoS) concerns of high-performance, innovative services. In today’s complex telecommunications network, coupled with continuous traffic growth, and users’ demand for higher speeds, it is vital for mobile operators to allocate their available resources efficiently. This paper focuses on the joint resource allocation problem of delivering adaptive video streams to users located in different slices of a wireless network enabled by MEC, SDMN, and C-RAN technologies. It proposes a novel Cross-Layer QoE-Driven Bitrate Allocation (CLQDBA) algorithm, that aims to improve system utilization by using information from the higher layers regarding traffic patterns and desired video quality of HTTP Adaptive Streaming (HAS) users. The mixed-integer nonlinear program is formulated, taking into account network slice requirements, radio resource limitations, storage and transcoding capacity of MEC servers, and users’ quality of experience. CLQDBA is a low complexity greedy-based algorithm aims to maximize users’ quality of experience (QoE) and minimize the deviation between the achievable throughput at the MAC-layer for users and the value of allocated bit rates for video frames at the application layer. The simulation result shows that compared to the baseline scheme, our introduced algorithm, on average, achieves a 15% higher system utilization, 17% higher video quality, and 13% improvement of Jain’s Fairness index for HAS users.

The widespread availability of smart computing and display devices such as mobile phones, gaming consoles, laptops, and tethered/untethered head-mounted displays has fueled an increase in demand for omnidirectional (360°) videos. 360° video applications enable users to change their viewing angles while interacting with the video during playback. This allows users to have a more personalized and interactive viewing experience. Unfortunately, these applications require substantial network and computational resources that the conventional infrastructure and end devices cannot support. Recently proposed viewport adaptive fixed tiling solutions stream only relevant video tiles based on user interaction with the virtual reality (VR) space to use existing transmission resources more efficiently. However, achieving real-time accurate viewport extraction and transmission in response to both head movements and bandwidth dynamics can be challenging, which can impact the user’s Quality of Experience (QoE). This article proposes innovative dynamic tiling-based adaptive 360° video streaming solutions in order to achieve high viewer QoE. First, novel and easy-to-scale tiling layout selection methods are introduced, and the best tiling layouts are employed in each adaptation interval based on the prediction-assisted visual quality metric and the observed viewport divergence. Second, a novel proactive tile selection approach is presented, which adaptively extracts tiles for each selected tiling layout based on two low-complex viewport prediction mechanisms. Finally, a practical dynamic tile priority-oriented bitrate adaptation scheme is introduced, which uniformly distributes the bitrate budget among different tiles during 360° video streaming. Extensive trace-driven experiments are conducted to evaluate the proposed solutions using head motion traces from 48 VR users for five 360° videos with tiling layouts of 4 × 3, 6 × 4, and 8 × 6 and segment durations of 1s, 1.5s, and 2s. The experimental evaluations show that the dynamic video tiling solutions achieve up to 11.2% more viewport matches and an average improvement in QoE of 9.7% to 18% compared to state-of-the-art 360° streaming approaches.

The exponential growth of data-hungry apps has led to an enormous increase in global data traffic. The recent worldwide pandemic has also significantly contributed to changing traffic patterns due to overloaded macrocells in the suburban residential areas, creating load balancing issues. 5G technology along with Heterogeneous Networks (HetNets), consisting of small cells, are promising solutions to address this problem. Still, the deployment of these small cells, labelled femtocells, brings a new challenge known as femtocells' co-tier interference that can hinder the performance of various 5G applications. The introduction of Machine Learning (ML)/Artificial Intelligence (AI) in the 5G architecture is considered a promising solution to tackle this issue. In this paper, we propose a machine learning-based algorithm, Machine Learning Multi-Classification and Offloading Scheme (MLMCOS) to mitigate co-tier interference in 5G HetNets. MLMCOS classifies users into multiple classes and uses a service-based-priority system to offload users experiencing co-tier interference. The proposed algorithm is compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). Simulation results show that MLMCOS outperforms some existing solutions in terms of throughput, delay and Packet Loss Ratio (PLR).

Bluetooth Low Energy (BLE) is one of the technologies that can be used for short-range communications. BLE beacons are small wireless devices that can store URLs and real-world data, including GPS locations and identifiers. This article proposes BeTwin, an efficient solution that integrates BLE beacons with a digital twin environment in order to enhance and customise a virtual 3D platform, bridging the real and virtual worlds. The beacons are used to bring information from real objects into the virtual platform. We extend the Eddystone messaging protocol and investigate the impact of beacon distance, energy levels, and number of beacons on the system performance. We also describe a testbed consisting of a virtual environment, a beacon reader along with beacons, and assess its performance in terms of communications latency, Received Signal Strength Indicator (RSSI), and number of beacons.

Vehicle-to-everything communication (V2X) has recently attracted considerable attention in 5G and beyond 5G due to its potential benefits, including increased capacity, spectral efficiency, and delay reduction, as well as the ability to provide new services associated with intelligent transportation. Several modes of communication can be used for V2X communications, namely sidelink mode, cellular uplink/downlink mode, and combined modes of communication which enable vehicles to communicate directly with each other. The challenge, however, is to select the appropriate operating mode in accordance with the requirements of V2X services. To minimize the overall latency of the V2X communications links, we propose a novel latency-aware mode coordination (LAMOC) algorithm that constructs an association graph of the vehicular network’s elements and finds the feasible path that minimizes total latency. The proposed solution is assessed using a simulation-based analysis. Simulation results have demonstrated that the proposed solution improves network performance in terms of reliability and latency in various scenarios.

In recent years, there has been a growing demand for robotic environment perception and autonomous driving due to the increasing popularity of visual and geometry-based localization and mapping techniques, such as simultaneous localization and mapping (SLAM). To address this trend, this paper proposes the EMS-SLAM framework, which utilizes cooperative adaptive wireless communication between servers and multi-robot agents to enhance environment perception and self-localization efficiency and accuracy. EMS-SLAM can reduce mapping time and CPU and memory utilization of individual robots while maintaining high accuracy OctoMap based on multi-map fusion and optimization. EMS-SLAM’s effectiveness and real-time performance have been validated and tested on publicly available datasets and real robots for real-world operations. The experimental results demonstrate that EMS-SLAM can reduce the CPU utilization of a single robot by approximately 10% and improve the efficiency of large-scale SLAM. The constructed OctoMap achieves centimeter-level accuracy. EMS-SLAM provides reliable, agile, and energy-efficient assistance for large-scale environment perception of robots.

Vehicular Ad-Hoc Networks (VANETs) are very promising in the context of intelligent transportation systems. VANETs are vulnerable to various types of attacks, including black hole and gray hole attacks, which can disrupt or intercept communication and compromise the security and reliability of the network. To address this issue, we propose a method for detecting and preventing malicious vehicles activity in VANETs by utilizing a trustworthy routing technique based on Q-learning (QL-TRT). Our approach, which is formulated based on the Markov Decision Process, allows the vehicle to choose the best neighbor for routing and avoid malicious neighbors. To evaluate the trustworthiness and reliability of the links between pairs of vehicles, we consider factors such as packet forwarding ratios, energy consumption, and expected transmission time. Q-learning is used to learn the trust value of links and select the most trusted route from source to destination. The evaluation results demonstrate the effectiveness of QL-TRT in detecting black hole and gray hole attacks, while ensuring the communication performance in VANETs.

Multimedia streaming for the Internet of Vehicles has the potential to enhance road safety and transport efficiency for autonomous vehicles, and the in-car experience for passengers. MPEG-Dynamic Adaptive Streaming over HTTP (MPEG-DASH) framework has been widely deployed to optimize video streaming with respect to end-user Quality of Experience (QoE). However, existing heuristic-based, reinforcement learning-based, or fuzzy-based adaptive algorithms, which use complex control laws and decision-making processes, are not well-suited to handle the non-stationary nature of road traffic environments. Consequently, these solutions often struggle to deliver optimal performance across multiple QoE objectives and under diverse network conditions. In this paper, we introduce FLAME, a novel adaptive multimedia streaming solution based on advanced fuzzy logic. FLAME incorporates interactive membership functions and fuzzy rules in its two variants, FLAME7 and FLAME5, resulting in reduced model complexities and training overheads. FLAME is adaptable to diverse video client settings and QoE goals. Our trace-driven experimental results demonstrate that FLAME solutions offer an improved uninterrupted streaming experience for connected vehicles. On average, FLAME outperforms other state-of-the-art solutions such as PENSIEVE, BOLA, FESTIVE, BBA, and ELASTIC by achieving 11.7% higher QoE.

Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delay-sensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the α-fair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with α-fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the long-term average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

The latest evolution of wireless communications enables user access rich Virtual Reality (VR) services via the Internet, including while on the move. However, providing a premium immersive experience for massive number of concurrent users with various device configurations is a significant challenge due to the ultra-high data rate and ultra-low delay requirements of live VR services. This paper introduces an innovative multi-user cost-efficient crowd-assisted delivery and computing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G-and-beyond heterogeneous networks (5G-HetNets). The proposed MEC-DC framework is based on three main solutions. First is a novel buffer-nadir-based multicast (BNM) mechanism for VR transmissions over 5G-HetNets. BNM ensures smooth and synchronized user viewing experience by maximizing the average playback buffer-nadir of all participants with stochastic optimization. Second and third are practical distributed algorithms: the cost-efficient multicast-aware transcoding offloading (MATO) and crowd-assisted delivery algorithm (CAD) which optimize jointly multicast delivery and video transcoding. The algorithms optimality and complexity were investigated. The proposed MATO-CAD solution was evaluated with real datasets, trace-driven numerical simulations, and prototype-based experiments. The trace-driven experimental results showed how the proposed solution provides 18% throughput improvement, lowest delay and best playback freeze ratio in comparison with three other state-of-the-art solutions.

The exponential demand for multimedia services is one reason behind the substantial growth of mobile data traffic. Video traffic patterns have significantly changed in the past two years due to the coronavirus disease (COVID-19). The worldwide pandemic has caused many individuals to work from home and use various online video platforms (e.g., Zoom, Google Meet, and Microsoft Teams). As a result, overloaded macrocells are unable to ensure high Quality of Experience (QoE) to all users. Heterogeneous Networks (HetNets) consisting of small cells (femtocells) and macrocells are a promising solution to mitigate this problem. A critical challenge with the deployment of femtocells in HetNets is the interference management between Macro Base Stations (MBSs), Femto Base Stations (FBSs), and between FBS and FBS. Indeed, the dynamic deployment of femtocells can lead to co-tier interference. With the rolling out of the 5G mobile network, it becomes imperative for mobile operators to maintain network capacity and manage different types of interference. Machine Learning (ML) is considered a promising solution to many challenges in 5G HetNets. In this paper, we propose a Machine Learning Interference Classification and Offloading Scheme (MLICOS) to address the problem of co-tier interference between femtocells for video delivery. Two versions of MLICOS, namely, MLICOS1 and MLICOS2, are proposed. The former uses conventional ML classifiers while the latter employs advanced ML algorithms. Both versions of MLICOS are compared with the classic Proportional Fair (PF) scheduling algorithm, Variable Radius and Proportional Fair scheduling (VR+PF) algorithm, and a Cognitive Approach (CA). The ML models are assessed based on the prediction accuracy, precision, recall and F-measure. Simulation results show that MLICOS outperforms the other schemes by providing the highest throughput and the lowest delay and packet loss ratio. A statistical analysis was also carried out to depict the degree of interference faced by users when different schemes are employed.

Providing premium panoramic livecast services to worldwide viewers considering their ultra-high data rate and delay-sensitivity is a significant challenge in the current network delivery environment. Therefore, it is important to design an efficient way of improving viewer quality of experience while conserving bandwidth resources. In this context, this paper introduces a novel cost-efficient federated transmission framework calledFedLiveand a set of algorithms to support it. First a gradient-based clustering method is proposed to group the geo-distributed viewers with similar viewing behavior into content delivery alliances by exploiting the geometric properties of the gradient loss. Next, aReinforcedVariationalInference (RVI) structure-based approach is proposed to assist with the collaborative training of the viewer field of view (FoV) prediction model while also accelerating the tile delivery process. A novel prediction-based asynchronous delivery algorithm is designed in which both the high accuracy FoV prediction and efficient live 360$^\circ$video transmission are achieved in a decentralized manner. FedLive was implemented for testing and an open source code is made available. Finally, the proposed solution was evaluated against a benchmark and three alternative state-of-the-art solutions using a real-world dataset. The experimental results show that our approach provides the highest prediction accuracy, better service performance, and saves bandwidth when compared with the other solutions.

In the context of the latest growing popularity of live video streaming, ensuring high video quality has become one of the most significant challenges faced by all live streaming platforms. Insufficient uplink bandwidth is an important factor that influences these live video transmissions, affecting their bitrate and latency and consequently the associated video streaming quality. This paper proposes a novel flexible super-resolution-based video coding and uploading framework (FlexSRVC) that improves the quality of live video streaming in limited uplink network bandwidth conditions. FlexSRVC includes a flexible video coding scheme, which compresses high-resolution key and non-key video frames to a lower bitrate in order to reduce the upload delay. A new flexible bitrate adaptation algorithm is also proposed to select dynamically the number of frames to be compressed and the compression ratio by jointly considering uplink network conditions and available cloud computing resources. Trace-driven emulations demonstrate that FlexSRVC provides the same quality while reducing up to 25% of the required bandwidth compared to the original encoding method (H.264). FlexSRVC improves users' QoE by at least 50% compared to a super resolution-based method which employs reconstruction of all video frames in uplink bandwidth constrained conditions.

Improving user experience during the delivery of immersive content is crucial for its success for both the content creators and audience. Creators can express themselves better with multisensory stimulation, while the audience can experience a higher level of involvement. The rapid development of mulsemedia devices provides better access for stimuli such as olfaction and haptics. Nevertheless, due to the required manual annotation process of adding mulsemedia effects, the amount of content available with sensorial effects is still limited. This work introduces an innovative mulsemedia-enhancement solution capable of automatically generating olfactory and haptic content based on 360 video content, with the use of neural networks. Two parallel neural networks are responsible for automatically adding scents to 360 videos: a scene detection network (responsible for static, global content) and an action detection network (responsible for dynamic, local content). A 360 video dataset with scent labels is also created and used for evaluating the robustness of the proposed solution. The solution achieves a 69.19% olfactory accuracy and 72.26% haptics accuracy during evaluation using two different datasets.

The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes.

The software-defined wide area network (SD-WAN) is considered one of the most promising paradigms for the next generation enterprise networks. However, SD-WAN users usually suffer from significant propagation delays due to the remotely deployed cloud centers. The requirements of delay-sensitive business services make the use of fog nodes and optimal resource allocation methods very important. In this paper, we propose a revenue-oriented service offloading method to improve the efficiency of SD-WAN through fog-cloud collaboration. Aiming at maximizing the service revenue, we formulate a coupled combinatorial optimization model to jointly allocate computation and communication resources in both the fog node and the cloud. To solve this problem, we propose a service offloading decision-making method based on the counterfactual regret minimization (CFR) principle according to the workload state of the fog node. This method reduces the time complexity of solving the original problem from exponential to polynomial by providing an approximate optimal solution, and achieves a good performance that is very close to the optimal solution in terms of service efficiency. Simulation results show that our method outperforms benchmark approaches in terms of both effectiveness and efficiency.

Achieving high Quality of Service (QoS) is one of the important goals in the latest 5G Heterogeneous Networks (HetNets) environments. However, ensuring fairness among users with Reduced Power Consumption (RPC) is a major challenge. Although several studies have examined the joint issue of User Association (UA), Resource Allocation (RA), and Power Allocation (PA), there is still no optimal solution that achieves QoS fairness and RPC with low complexity and processing time. This paper proposes the Power-Performance Efficient Adaptive Genetic Algorithm (P2EAGA) for solving the UA-RA-PA problem in HetNets. The UA-RA sub-problem is formulated as a ‘0/1’ Multiple Knapsack Problem (MKP) with constraints on the maximum capacity of Base Stations (BSs) along with the transport block size index. Its sub-optimal solution is given as input to the second MKP, formulated to solve the PA sub-problem with constraints on the total BS power capacity. Simulation results show that P2EAGA outperforms existing schemes in terms of variability, fairness, RPC, and QoS, including throughput, packet loss ratio, delay, and jitter. Simulation results also show that P2EAGA generates solutions that are very close to the optimal global solution compared to the Default Genetic Algorithm.

Virtual reality (VR) content, including 360° panoramic video, provides users with an immersive multimedia experience and therefore attracts increasing research and development attention. However, the requirement of high bandwidth and low latency of virtual reality service demand puts forward greater challenges to the current infrastructure, especially mobile networks. Inspired by the sharable nature of virtual reality content tiles, we further considered the potential opportunities for computing, caching, and multicast to address the challenges of transmission of panoramic content. This paper proposes a novel transcoding-enabled VR video caching and delivery framework for edge-enhanced next-generation wireless networks. Firstly, an edge cooperative caching scheme based on multi-agent reinforcement learning is introduced to improve the utilization efficiency of computing and storage resources, and then reduce service delay. Second, a two-tier NOMA-based base station-multicast group matching mechanism is designed to solve the collaboration challenge during the edge delivery process. A series of experiments have demonstrated the advantages of the proposed scheme in terms of cache hit rate, latency and other aspects in comparison with alternative approaches.

The latest increase in HTTP-based adaptive video streaming over the Internet enables a growing number of clients to compete for a shared bottleneck bandwidth. This competition may affect users’ Quality of Experience (QoE) negatively, especially in terms of fairness and stability. This paper presentsFlex-Steward, a solution that performs multi-client joint QoE optimization for adaptive video streaming during bottleneck bandwidth sharing. Joint QoE optimization refers to improving QoE fairness among clients with various video devices and availing from differentiated services with different priorities. Flex-Steward deploys an adaptive bitrate delivery algorithm based on Neural Networks (NN) and reinforcement learning at the network edge. It relies on a trained NN model to make appropriate bitrate recommendations in terms of video chunks to be requested by clients sharing the same bottleneck bandwidth. Flex-Steward is assessed in comparison with alternative state-of-the-art algorithms under different network conditions using a real-life prototype. Results show how Flex-Steward reduces the unfairness in terms of joint QoE optimization with between 10.9% and 41.7%.

The latest Heterogeneous Network (HetNet) environments, supported by 5th generation (5G) network solutions, include small cells deployed to increase the traditional macro-cell network performance. In HetNet environments, before data transmission starts, there is a user association (UA) process with a specific base station (BS). Additionally, during data transmission, diverse resource allocation (RA) schemes are employed. UA-RA solutions play a critical role in improving network load balancing, spectral performance, and energy efficiency. Although several studies have examined the joint UA-RA problem, there is no optimal strategy to address it with low complexity while also reducing the time overhead. We propose two different versions of simulated annealing (SA): Reduced Search Space SA ($RS^{3}A$) and Performance-Improved Reduced Search Space SA ($PIRS^{3}A$), algorithms for solving UA-RA problem in HetNets. First, the UA-RA problem is formulated as a multiple knapsack problem (MKP) with constraints on the maximum BS capacity and transport block size (TBS) index. Second, the proposed$RS^{3}A$and$PIRS^{3}A$are used to solve the formulated MKP. Simulation results show that the proposed scheme$PIRS^{3}A$outperforms$RS^{3}A$and other existing schemes such as Default Simulated Annealing (DSA), and Default Genetic Algorithm (DGA) in terms of variability and DSA and$RS^{3}A$in terms of Quality of Service (QoS) metrics, including throughput, packet loss ratio (PLR), delay and jitter. Simulation results show that$PIRS^{3}A$generates solutions that are very close to the optimal solution.

Computational offloading, as an effective way to extend the capability of resource-limited edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for coping with delay-sensitive services. However, on one hand, applications commonly include several subtasks with dependent relations and on the other hand, the dynamic changes in network environments make offloading decision-making become a coupling and complex NP-hard problem, difficult to address. This paper proposes an intelligent Computational Offloading scheme for Dependent IoT Application (CODIA), which decouples the performance enhancement problem into two processes: scheduling and offloading. First, a prioritized scheduling strategy is designed and its complexity is analyzed. Then, an offloading algorithm with offline training and online deployment is introduced. Due to the temporal continuity between subtasks, the dependency relation is transformed into a transition of device state, and the overhead for the whole application is considered to be the long-term benefit.CODIAleverages an Actor-Critic-based solution, where the IoT devices are able to deploy intelligent models and dynamically adjust the offloading strategy to achieve low latency, while controlling energy consumption. Finally, a series of experiments are conducted to verify the robustness and efficiency of the proposed solution in terms of convergence, latency, and energy consumption.

With the development of communication technology, emerging multimedia (e.g. virtual reality) can provide users with more immersive service experience. However, due to the ultra-high rendering and splicing requirements of multimedia content, the higher demand for computing resources is put forward for the playback device. The anomalies of energy consumption and latency caused by such computationally intensive tasks hinder the practical application of emerging multimedia technology in mobile networks. In this regard, this paper proposes an edge computing assisted multimedia service optimization scheme (ECMSO) to broaden the computing capacity of the viewer(i.e. requester), so as to ensure that content can be served in time and reduce the energy cost of computation from the perspective of executor and requester, respectively. First, a computational offloading scheme based on deep reinforcement learning is designed. It optimizes intelligently the energy consumption while meeting the latency requirements of the requester. Secondly, a heuristic algorithm to allocate power, bandwidth, and computing resources for candidate executors is proposed. Finally, a series of simulation experiments are conducted to demonstrate the effectiveness of our proposed scheme.

Software-defined wide area network (SD-WAN) is a new type of network architecture that has developed rapidly in recent years. SD-WAN inherits the centralized control ar-chitecture of Software-defined networking (SDN), but supports more diverse access methods and equipment types and covers a wider area. It is also associated with greater uncertainty in the network environment. These characteristics make the fault management of the SD-WAN controller more challenging, so that the existing SDN-based data backup methods cannot adapt to SD-WAN scenarios. This paper proposes an SD-WAN-oriented Distributed Data Backup and Recovery method (DDBR) based on an improved secret sharing algorithm. To deploy this method, we design an online-offline dual backup framework based on the data freshness requirements of the controller. Under this framework, dynamic data of the controller is divided into different shares, and then stored into the storage of switches. When the controller fails, data recovery can be performed on the backup controller quickly, which greatly improves the network availability. The outstanding feature of the proposed DDBR method is that it ensures the integrity and confidentiality of the backup data in an unreliable network environment, even when some of the storage nodes fail. Evaluation results on file backup example show that the proposed solution has significant advantages over existing methods in terms of backup data storage size and backup success rate.

Recent years have witnessed an outstanding increase in popularity of Crowdsourced Livecast Services (CLS), which is the latest trend in social media. In CLS, transcoding enormous video contents from massive broadcasters and providing high-quality CLS for global viewers with heterogeneous devices are computation-intensive as well as time-consuming. There are some schemes that design viewer-assisted transcoding scheme, but it is challenging to achieve an efficient and fair task assignment due to the dynamic of computing and communication resources. This paper introduces a viewer-assisted CLS framework and focuses on proposing an innovative fairness-guaranteed task assignment scheme, which is a key challenge in this context. Considering the dynamic nature of viewers’ computing and communication resources and stability, a dynamic programming problem with fairness and QoS constraints is formulated. To solve the problem, we devise a Fair Bandit (FB) algorithm based on the Combinatorial Multi-Armed Bandit (CMAB). Finally, the effectiveness of proposed scheme is demonstrated by trace-driven simulations.

Intensive video transcoding and data transmission are the most crucial tasks for large-scale Crowd-sourced Livecast Services (CLS). However, there exists no versatile model for joint optimization of computing resources (e.g., CPU) and transmission resources (e.g., bandwidth) in CLS systems, making maintaining the balance between saving resources and improving user viewing experience very challenging. In this paper, we first propose a novel universal model, called Augmented Graph Model (AGM), which converts the above joint optimization into a multi-hop routing problem. This model provides a new perspective for the analysis of resource allocation in CLS, as well as opens new avenues for problem-solving. Further, we design a decentralized Networked Multi-Agent Reinforcement Learning (MARL) approach and propose an actor-critic algorithm, allowing network nodes (agents) to distributively solve the multi-hop routing problem using AGM in a fully cooperative manner. By leveraging the computing resource of massive nodes efficiently, this approach has good scalability and can be employed in large-scale CLS. To the best of our knowledge, this work is the first attempt to apply networked MARL on CLS. Finally, we use the centralized (single-agent) RL algorithm as a benchmark to evaluate the numerical performance of our solution in a large-scale simulation. Additionally, experimental results based on a prototype system show that our solution is superior in saving resources and service performance to two alternative state-of-the-art solutions.

The Multi-access Edge Computing (MEC) paradigm offers cloud-computing support to rich media applications, including Dynamic Adaptive Streaming over HTTP (DASH)-based ones at the edge of the network, close to mobile users. MEC servers, typically deployed at base stations (BS), help reduce latency and improve quality of experience (QoE) of video streaming. Unfortunately the communications involving mobile users require handovers between BSs and these influence both transmission efficiency because of the relative position of the MEC servers and transit cost. At the same time, serving MEC for a mobile user should not necessarily be changed when handover occurs. This paper introduces QoE Ready to Respond (QoE-R2R), a QoE-aware MEC Selection scheme for DASH-based mobile adaptive video streaming for optimizing video transmission in a MEC-supported network environment. Simulation-based testing shows that the proposed (QoE-R2R) scheme outperforms some traditional alternative solutions. Compared to hit rate and delay-based schemes, QoE-R2R reduces by 27.6% transmission time and improves with 6.2% QoE.

The integration of software-defined networking (SDN) into legacy networks causes both operational and deployment issues. In this context, this article proposes a novel approach, called An Intelligent Solution for Improved Performance of Reliable and Time-sensitive Flows in hybrid SDN-based fog computing IoT systems (IHSF). The proposed IHSF approach has three solutions: 1) a novel algorithm to deploy SDN switches between legacy switches to improve network observability; 2) a ${K}$ -nearest neighbor regression algorithm to predict in real time the reliability of legacy links at the SDN controller based on historic data; this enables the SDN controller to make timely decisions, improving system performance; and 3) a reliable and time-sensitive deep deterministic policy gradient algorithm (RT-DDPG), which optimally computes forwarding paths in hybrid SDN-F for time-critical traffic flows generated by IoT applications. The simulation results show that our proposed IHSF solution has a better performance than the existing approach in terms of network observability time, number of disturbed flows, end-to-end delay, and packet delivery ratio.

Recently, innovative technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Multi-Sensorial Media (mulsemedia) have introduced new sensorial effects including vibration, smell, airflow, etc. to human life. These effects which have been largely deployed for entertainment, and gaming have positively impacted user satisfaction. This paper explores the potential of mulsemedia in the education context. It describes a novel Dynamic Adaptive Streaming over HTTP (DASH)-based Multi-sensory Media Delivery Solution (DASHMS) which supports adaptive mulsemedia content distribution based on the operational environment which includes network, device, and user settings.DASHMS was evaluated in a real-life educational experiment involving 44 students in an Irish university. The evaluation focused on both learner satisfaction, and the impact on learning. The results demonstrate the potential of adaptive multi-sensorial media delivery to result in a statistically significant increase in user experience. In terms of benefit to learning outcomes however, it was only memory recall which was statistically improved in the experiment.

The outstanding increase in video traffic, puts increasing pressure on network transmission. Since the Dynamic Adaptive Streaming over HTTP (DASH) adjusts the delivery to the dynamic network conditions, it has emerged as a popular approach for video transmissions. However, bitrate switching and video rebuffering may still occur and influence negatively quality of experience (QoE). Additionally the popular videos are transmitted multiple times, which leads to high bandwidth consumption, despite large transmission redundancy. In this context, we propose a Cooperative Learning-based scheme for the smart Edge servers with cAching and Prefetching (CoLEAP) to improve the QoE of adaptive video streaming. CoLEAP employs edge servers which cache the most beneficial contents to reduce redundant video transmissions and prefetches content to decrease network transmission delay. Considering user-related information and the state of network, CoLEAP intelligently makes the most advantageous decisions of caching and prefetching by employing a novel QoE-oriented deep neural network model. To demonstrate the performance of our scheme, we test the proposed solution in comprehensive simulated scenarios and against four alternative solutions. When compared with the existing schemes, CoLEAP increases average bitrate by up to 181.8%, reduces video rebuffering by up to 70.8% as well as decreases response time by up to 28.0%. These values result in minimum improvements of 57.4% and 29.0%, respectively in terms of cache hit rate and QoE.

Virtual Reality (VR) popularity is increasing as it is becoming more affordable for end users. Available VR hardware includes low-end inexpensive devices like Google Cardboard and high-end ones like HTC Vive or Oculus Rift, which are more expensive headsets. Using VR as a platform for content delivery allows better user engagement than other traditional methods, as VR headsets remove external distractions. Multiuser VR applications provide shared experiences where users can communicate and interact in the same virtual space. This shared environment, however, introduces challenges regarding network performance, quality of service (QoS) and sessions privacy. This paper presents a multi-user VR application and aims to evaluate network behaviour in a number of scenarios, including real VR headsets (i.e. Oculus Rift), as well as simulated ones. This QoS analysis is important for the understanding of how many VR users can be simultaneously connected with high image quality.

This paper presents Adaptive VR (AVIRA), a scheme that implements a Virtual Reality (VR) content-aware prioritisation transport to extend Multipath TCP (MPTCP) functionalities and improve its performance. To do so, AVIRA monitors the subflows operation and forecasts subflows' performance by applying an Machine Learning (ML) approach to evaluate a set of features - such as latency and throughput - for every subflow available. This ML approach forecasts the performance of these features through linear regression and applies a linear classifier by using a weighted sum on the forecast results. When the traffic of a specific VR component is detected, AVIRA performs its prioritisation scheme by redirecting packets to the subflow with the best set of forecasted features. AVIRA outperforms the algorithms used for comparison and shows that the use of an ML approach in a “low-level” application is viable, especially in situations where the network features under scrutiny are subject to higher variations. In these scenarios, the AVIRA scheme can be outstandingly efficient.

5G technology is expected to enable a plethora of new applications with distinct requirements. Provisioning resources to accommodate such applications implies having a flexible network infrastructure that can be tailored to the specific needs of each application. This can be achieved through network slicing. Still, several applications might request network slices, but their request may not be fulfilled due to lack of resources or lack of coverage and provisioning such resources is a cumbersome task. This paper describes an architecture that facilitates the dynamic leasing of resources among network operators to support cross-domain services. The cornerstone of this architecture is a brokering layer, called DBB, that relies on a blockchain-based bidding system to request resources and evaluate resource provisioning offers. The paper also presents a simulation-based use case scenario that illustrates the need for DBB and which was used to evaluate the performance of the proposed architecture.

The latest increase in multi-view video solutions, including those for telepresence, commercial conferencing, remote collaboration, etc. requires support for the high-quality delivery of large amounts of content data. Meanwhile, the extensive proliferation of wireless network access technology and multiple network interfaces on modern devices prompt the network transmission performance over various access networks. Diverse multipath-based multi-view streaming and adaptive delivery solutions were proposed, but they do not enable differentiation between streams. This paper proposes MVP-DASH, a priority-aware adaptive multi-view video streaming scheme based on the MPEG-DASH framework. MVP-DASH enables improved visual quality for high-priority streams, while maintaining acceptable quality levels for low-priority streams, primarily when delivered over a dynamic network environment. The experimental evaluation of the MVP-DASH demonstrates the effectiveness of the proposal in terms of achieving higher video quality and fewer video quality switches in comparison with alternative approaches.

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, Network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution.

Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS's impact on throughput, packet loss ratio (PLR), peak-to-signal-noise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed.

By pushing computing functionalities to network edges, backhaul network bandwidth is saved and various latency requirements are met, providing support for diverse computation-intensive and delay-sensitive multimedia services. Due to the limited capabilities of edge nodes, it is very important to decide which services should be provided locally. This paper investigates the cloud-edge service offloading problem. Different from prior works which only give the proportion of computation offloading with constraint of computing capacity, we also take the storage space into account and determine the computing status of each service. We formulate the problem as a Markov decision process whose goal is to maximize the long-term average reduction of delay. The problem is hard to be solved with traditional methods because of the extremely large action space and lack of information about transition probability. Instead, this paper proposes an innovative deep reinforcement learning method to solve it. The proposed multi-update reinforcement learning algorithm introduces a novel exploration strategy and update method, which reduce dramatically the size of the action space. Extensive simulation-based testing shows that the proposed algorithm has fast convergence and improves the system performance more than other three alternative solutions do.

The Internet of Things (IoT) is a growing network of physical objects where the devices are connected to the Internet through unique addressing schemes and multiple protocols. The increase of IoT devices in the recent years presents significant challenges in terms of security, authentication and usability. The recently introduced Social Internet of Things (SIoT) tries to address these challenges with the virtualisation of IoT devices and the use of an infrastructure where people and IoT devices can communicate with each other, both in the real-world and virtual-world, through a common platform. In the proposed SIoT architecture, IoT devices can be operated by virtual reality (VR) headsets and Twitter, a social media platform. The aim of the platform is to allow users to seamlessly operate IoT devices, using their preferred interface: remotely with text messages (i.e.tweets) and VR headsets or operate the IoT devices directly. This paper also describes the implementation of a testbed and presents the performance analysis of the solution, demonstrating its feasibility and low latency.

Recently, fabrication laboratories (Fab Labs) have been shown to have a great impact on learners' academic and personal progress. As a result, an increasing effort is being put to integrate Fab Labs into schools' curricula. Yet, owing to the high cost of setting up and maintaining Fab Labs as well as the lack of sufficient funding for most schools and universities, only a limited number of institutions can afford them. In this article, we propose a new concept called Fabrication-as-a-Service (FaaS) that uses Internet of Things to democratize access to Fab Labs via enabling a wide learning community to remotely access these computer-controlled tools and equipment over the Internet. It employs a two-tier architecture consisting of a hub, deployed in the cloud, and a network of distributed Fab Labs. Each Fab Lab interacts with the hub and other digital labs via a Fab Lab Gateway. This is to support scalability and high availability of fabrication services as well as ensure the system's security. FaaS also adopts an innovative master-slave approach that uses inexpensive external hardware to monitor and control the activity of expensive fabrication equipment. This article also describes the FaaS deployment in the context of the European Union Horizon 2020 NEWTON project. Multiple scenarios have been deployed to fully illustrate the benefits of the FaaS architecture and to assess the performance of its communication protocol stack.

The recent growth in both number of high specification mobile devices and network multimedia data demand, has made difficult supporting access to the multimedia content at high user Quality of Experience (QoE) levels. It is even more challenging to support offering such services adjusted to user specific requirements in current heterogeneous wireless network environments (HWNE). This paper proposes a novel Prioritized Adaptive Real-time Multi-user Access Network Selection framework (P-ARMANS) which employs a Markov Decision Process (MDP) solution to perform improved bandwidth resource allocation. P-ARMANS enables load balancing during multimedia delivery when users with diverse priority and different device screen resolutions access various services. Modeling and simulations show how P-ARMANS distributes content with different types of traffic and load among typical and business users at higher QoE levels compared to a classic no-priority approach.

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

Virtual labs are increasingly used both as an alternative to physical labs or as a complementary technology enhanced (TEL) solution for STEM education. Virtual labs enable students to conduct experiments in a controlled environment at their own pace. However, despite much research on personalisation and adaptation in the TEL area, most virtual labs that have been developed lack personalisation features. This paper presents results from a study with 78 secondary school students, aimed at evaluating an interactive personalised virtual lab called Atomic Structure. The virtual lab integrates personalisation, interactive experimentation, videos, e-assessment and gamification, to provide an engaging environment for learning chemistry concepts related to atoms, isotopes and molecules. The evaluation study followed a multi-dimensional methodology to assess the effectiveness of the virtual lab in terms of knowledge achievement, learner motivation and usability. The results show that the experimental group that learned with the virtual lab achieved statistically significant higher knowledge than the control group that attended a traditional teacher led session. The experimental group also had higher increase than the control group for different motivation dimensions between the pre and post questionnaires. The usability results showed that most students found the virtual lab useful, easy to use and liked/loved its features such as videos, quizzes and interactive atom builder.

As an important scenario under the 5G, the Internet of things (IoT) is undertaking countless computing tasks, which are obtained from real life. However, for IoT devices, due to the limited computing resource and battery capacity, it is difficult to cope with the diversified incoming computing tasks. To this end, this paper studies the IoT task computing expenditure problem with the assistance of fog computing and cloud center. We firstly propose a game theoretic task computing framework (GTTC) to ease the competition of multi-nodes by taking into account the expected benefit of each computing node. Then, we put forward the concept of tendency-oriented priority (TOP) to coordinate the scheduling order between multi-tasks of fog computing node for further reducing the expenditure. Finally, the effectiveness of the proposed mechanism is verified by sufficient experimental simulations in terms of a wide set of performance metrics.

Multipath TCP (MPTCP) can take advantage of multiple paths to transmit data and has been deeply optimized by many researchers. However, most researchers only devote themselves to improve the transmission performance, neglecting the price cost which is another factor that users are concerned about. This paper proposes a novel stochastic optimal scheduler for MPTCP (SOS-MPTCP) that utilizes Lyapunov optimization technique in software defined wireless network (SDWN). SOS-MPTCP analyzes and solves the trade-off problem between the performance and price cost from users' perspective. Besides, with the help of centralized optimization in SDWN architecture, the controller can feed status information of each path back to mobile terminals for SOS-MPTCP to make decisions. SOS-MPTCP includes three control decisions: 1) packets admission control; 2) packets distribution control; 3) data traffic purchasing control. SOS-MPTCP aims to maximize the throughput and minimize the price cost for users. Experiment results have proved the efficiency of trade-off optimization and the transmission system can achieve the expected stability.

This paper studies transmission performance in a coexistence scenario when a secondary communication network is deployed in co-channel mode within TV broadcast bands. Keeping in view the differences in timing and spectral characteristics between the primary and secondary transmissions, subcarrier-level interference at the primary is computed in presence of time-and-frequency overlapped secondary transmission. This estimation is validated experimentally as well as via simulations. Specifically, the possibility of coexistent secondary transmission near the primary broadcast receivers is experimentally evaluated in an emulated DVB-T2 transmission environment. The analytical and experimental studies demonstrate that the effects of interference at the primary receiver are within tolerable range even when the secondary transmitter transmits at high power with appropriately chosen time-frequency occupancy overlaps. The benefits are further enhanced when the agility of frequency and temporal overlaps are combined with power control in the secondary network.

Mobile video traffic is rapidly growing, putting significant pressure on current heterogeneous wireless networks. Common data is traditionally requested by multiple users from server or cache devices. This results in the same data being sent across the network multiple times causing unnecessary congestion. The proposed cooperative solution allows neighbouring devices to share content that was previously received with interested peers. In this paper, a Mobile aware Quality-oriented Cooperative Multimedia Delivery Solution is proposed which allows peer devices to identify neighbouring host devices while considering their own mobility. Simulated testing shows that the solution is capable of identifying the most suitable host while travelling at different speeds and maintaining a suitable quality level by adapting the content to meet network conditions. State-of-the-art comparative studies are outperformed by maintaining good quality with increasing speed in a mobile environment.

Due to its advantages in service flexibility, scalable video coding (SVC) has been widely used in Device-to-Device (D2D) network communication, which is an effective network technology in the fifth generation (5G) communication network. However, duo to the limited capacity of mobile device, the uninterrupted transmission communication is difficult to be maintained, which needs to be solved urgently. Therefore, in this paper, we introduce the maximum offloading traffic and energy cost ratio to estimate service performance and propose a heuristic algorithm based on the greedy strategy to maximize the offloading traffic because of the NP-hardness of the cache placement problem for SVC. Then, we improve the energy efficiency over D2D link by optimizing the transmission power. Finally, a series of detailed simulation experiments are conducted to analyze the relationship between offloading traffic and energy cost, which demonstrates that a tradeoff exists between the significant amount of traffic and approving energy efficiency.

Today, the dynamic network environment poses severe challenging issues to multimedia streaming services that account for an enormous part of network traffic all over the world. Dynamic adaptive streaming over HTTP (DASH) facilitates seamless video playback by allowing for dynamic adjustment of the video bitrate to the ongoing network situation. Despite several attempts, there is still a challenge to design solutions which use DASH to adjust video delivery to the dynamic network environment and achieve high user quality of experience levels. This paper presents a novel DASH-based throughput and buffer occupancy-based adaptation (TBOA) algorithm to provide an improved streaming experience for remote users. TBOA was compared against alternative solutions such as FDASH and SFTM in single- and multiple-client scenarios. Testing results show how TBOA selects higher video bitrates while performing fewer video bitrate switches and reduces the risk of buffer underrun in comparison with the competitors.

Providing support for energy efficiency and Quality of Service (QoS) is among the major challenges when designing solutions for video delivery over Wireless Multimedia Sensor Networks (WMSN). As video applications have high bitrate delivery requirements, an effective way is to enable the sensor nodes to transmit data over multiple paths in parallel, for instance by employing Multi-path TCP (MPTCP). However, in a dense network like WMSN, high traffic volume transmission may cause congestion, affecting all paths. In such a case, congestion control algorithms play a key role in order to support high QoS levels, including by employing retransmissions of lost data. However, data retransmissions result in energy consumption increases, which is an aspect of concern for WMSN services. In this paper, we propose eqCCMP, an energy-efficient congestion control scheme for MPTCP. The general idea of eqCCMP is to use low energy consumption paths to deliver data, while making sure good QoS levels are maintained. A fluid model is employed to formulate the operation of eqCCMP. The performance of eqCCMP is evaluated in comparison with other schemes including the vanilla MPTCP and ecMTCP. By considering an extra factor in the congestion avoidance phase for congestion window adjustment, the proposed eqCCMP outperforms the other solutions in terms of throughput and energy efficiency in a simulation environment.

Multipath TCP (MPTCP), as a promising transmission protocol, can aggregate the bandwidth of multiple paths in order to improve the transmission rate. However, due to the lack of perceiving the network status from lower layers, MPTCP cannot adaptively adjust the number of subflows, which will lead to network congestion or underutilization of network resources. Besides, plenty of packets will be out-of-order severely due to the diversity among the paths so that transmission performance degrades significantly. To address the problems mentioned above, we propose a novel QoS-driven and SDN-assisted MPTCP path selection scheme (QSMPS) for high-quality transmission service. QSMPS utilizes a scalable SDN-assisted approach to monitor and analyze network status information. Through matching service demand and the provided capacity of current network, the scheme calculates the optimal number of subflows, then distributes them to the least differential delay paths determinately. Simulation results show QSMPS outperforms the existing solutions by evaluating performance in Mininet emulator and Ryu controller.

The latest increasing popularity of high-speed railways (HSR) has stimulated growing demands for wireless Internet services in HSR networks, especially for video streaming. However, due to the high variability and unpredictability of wireless communications in HSR networks, it is still difficult for the existing solutions to provide high-quality video streaming services to HSR passengers. This paper addresses this crucial problem first by reporting on field experiments performed to investigate the characteristics of HSR networks. Then the paper formulates an intractable optimization problem for dynamic adaptive streaming over HTTP (DASH)-enabling service in HSR networks considering various factors, including packet loss, energy consumption, video service quality, etc. By leveraging Lyapunov optimization approaches, the formulated optimization problem is transformed into a queue stability problem which is of high scalability and generality. Moreover, in order to overcome the intractability of the initial optimization problem, the queue stability problem is further decomposed into three subproblems which can be easily solved individually. Finally, a novel joint stochastic DASH optimization (JSDO) mechanism consisting of three algorithms for the derived subproblems is proposed. Rigorous theoretical analyses and realistic dataset-based simulations demonstrate the effectiveness of the proposed JSDO mechanism.

Given the shortcomings of traditional networks, software-defined networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability, and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. This paper proposes HiDCoP, a distributed high-performance control plane for path computation in large-scale SDNs along with its related solutions. HiDCoP employs a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that HiDCoP outperforms existing schemes in terms of path computation time, end-to-end delay, and transmission overhead.

Teachers are facing many difficulties when trying to improve the motivation, engagement, and learning outcomes of students in Science, Technology, Engineering, and Mathematics (STEM) subjects. Game-based learning helps the students learn in an immersive and engaging environment, attracting them more towards STEM education. This paper introduces a new interactive educational 3D video game called Final Frontier, designed for primary school children. The proposed game design methodology is described and an analysis of a research study conducted in Ireland that investigated learner experience through a survey is presented. Results show that: (1) 92.5% of students have confirmed that the video game helped them to understand better the characteristics of the planets from the Solar system, and (2) 92.6% of students enjoyed the game and appreciated different game features, including the combination between fun and learning aspects which exists in the game.

As one of the key technologies for future 5G, Device- to-Device communications (D2D) offloads traffic to local by enabling mobile equipment directly communicating with each other, which perfectly supporting distributed applications and IoT scenarios. Integrating Information-centric networking (ICN) with D2D is becoming an attractive trend because of the superior advantages of inherent support of caching and name-based routing. Nevertheless, efficient caching in ICN D2D still remain problematic due to the low utilization of caching space and multicast feature of wireless scenarios. In this paper, we propose a novel optimal coded content caching mechanism for ICN-based 5G D2D. We first building a fluid-based model to describe how the roles of mobile nodes evolve with the user behaviors and caching strategy. We then accordingly formulate the coded caching problem as an optimization problem, which mainly considers the tradeoff between delivery latency and energy consumption. The existence of optimal solutions is proved theoretically. We further propose a Learn Tree- based Code Content (LTCC) mechanism to cluster the contents for content coding selection and an Optimal Coded Content Caching (O3C) algorithm to solve coded content caching problem. Finally, we conduct massive simulation tests to validate the performance of the proposed algorithm against the state-of-art solutions.

Given the shortcomings of traditional networks, Software-Defined Networking (SDN) is considered as the best solution to deal with the constant growth of mobile data traffic. SDN separates the data plane from the control plane, enabling network scalability and programmability. Initial SDN deployments promoted a centralized architecture with a single controller managing the entire network. This design has proven to be unsuited for nowadays large-scale networks. Though multi-controller architectures are becoming more popular, they bring new concerns. One critical challenge is how to efficiently perform path computation in large networks considering the substantial computational resources needed. In this paper, we propose DiSC, a distributed high-performance control plane for path computation in large SDNs. It endorses a hierarchical structure to distribute the load of path computation among different controllers, reducing therefore the transmission overhead. In addition, it uses node parallelism to accelerate the performance of path computation without generating high control overhead. Simulation results show that DiSC outperforms existing schemes, including the most recent ones, in terms of path computation time, path setup latency and end-to-end delay.

The recent fast development of wireless communications and smart devices has opened the avenue to supporting high quality video streaming services in vehicular networks. This growing trend towards enhanced video services and the inefficient content distribution of conventional IP networks have motivated the researchers to propose new Internet architectures that are more efficient for content distribution in general and in vehicular networks in particular. Information-centric networking (ICN) shifts the network paradigm from host centric to content centric, providing effective content distribution by named-based routing and in-network caching, which becomes a promising solution for sharing video streaming among vehicles. In this paper, we present a novel Family-Aware Pricing Strategy (FAPS) to accelerate video streaming dissemination over Information-Centric Vehicular Networks (ICVNs). We first classify the mobile users into multiple families by investigating user similar behaviors. Based on the family, an efficient video sharing scheme is proposed to support near end video fetching. In addition, a pricing-based video caching policy is also proposed to accurately optimize caching distributions. Simulation results show how our proposed strategy achieves better performance than other state-of-art solutions in terms of caching hit ratio, searching delay, freeze times and control overhead.

As an extension of Transmission Control Protocol (TCP), Multi-Path Transport Control Protocol (MPTCP) provides a reliable and streaming-oriented transmission service to the upper applications. However, when turning to the real-time multimedia transmission, the repeatedly retransmission of expired segments is unnecessary and inefficient. Thus, we propose a Message-Oriented Partial-Reliability (MO-PR) improvement for MPTCP in this paper. The MO-PR firstly extend the Partially-Reliability transmission scheme to MPTCP which allows the sender to abandon the invalid segment by notifying the receiver. Then, the Message-Oriented retransmission mechanism is designed to improve the discarding efficiency. Finally, the comparison-based simulation results show that MO-PR can effectively improve the transmission performance of multimedia in dynamic wireless networks.

This work proposes, describes and performs performance analysis of a Round-Trip Time (RTT)-aware packet delivery prioritisation algorithm (RDPA) for networked Augmented Reality/Nirtual Reality (ARNR) content distribution. In this approach, the proposed algorithm uses the built-in multipath delivery feature of MPTCP. RDPA tracks, identifies and redirects the priority packets through the subflow that presents best opportunities to deliver the content with the lowest latency in the next transmission interval. This subflow selection is based on a linear regression that analyses each subflow behaviour and identifies the best subflow in terms of latency. The assessment of this algorithm is performed in a Network Simulator 3-based simulation environment and indicates performance improvements varying from 8% to 36% (peak performance) when compared with the MPTCP default operation.

Internet of Things (IoT) networks handle multiple data types generated by numerous types of devices, with the additional challenge of delivering this data at high QoS levels. A great number of IoT applications require real-time updates and fresh data, in areas such as health, vehicular, UAVs and sensor monitoring (temperature, acceleration, motion, etc therefore, a metric such as the Age of Information (AoI) is useful to measure how recent the information is, considering the difference between the time it was generated and the time it is successfully delivered. In this paper, we propose an architecture consisting of IoT objects, which provide diverse services including video applications and CCTV monitoring, smart gateways, which support network connectivity, and a cloud-deployed platform utilised for resource management. The performance, quality and mobility challenges of IoT services are improved through an algorithm which uses Quality of Service (QoS), AoI, objects location and service relevance metrics in order to cluster the networked IoT objects, attach them to the most suitable smart gateway or relay device and improve the performance of their services.

Similar to IEEE 802.11e, the Wireless Access for Vehicular Environment (WAVE) uses the Enhanced Distributed Channel Access (EDCA) to provide service differentiation. Nevertheless, WAVE does not make use of the transmission opportunity (TXOP) parameter, i.e., only one packet can be transmitted per channel access. This fits well most safety applications as they usually transmit individual short messages. Yet, non-safety applications can witness a decline in their performance as they often transmit multiple long messages. In this paper, we propose an innovative scheme, called DTAS, that dynamically assigns TXOP limits to vehicles to improve non-safety applications' efficiency. DTAS targets vehicle-to-vehicle (V2V) communications and updates periodically its functionality to reflect changes in both network circumstances and mobility pattern between vehicles. To the best of our knowledge, no existing work has proposed something similar for V2V non-safety applications. Simulation results demonstrate that DTAS generates higher throughput compared to the conventional IEEE 802.11p.

Science teachers and researchers believe that students' disengagement from STEM area can be overcome by using interactive and fun-based computer educational games in order to support knowledge acquisition through direct experience. This paper presents a research study on the effectiveness of a new interactive educational 3D video game called Final Frontier. The game supports delivery of scientific knowledge on the Solar system to primary school students. A comprehensive case study that involved 30 children was conducted to evaluate the game. The results confirmed that the game supports high learning achievements through an enjoyable and fun learning environment entirely appreciated by children. The vast majority of the students (93%) have expressed their interest in learning other subjects through such an interactive computer game.

The extremely large number of devices available to the modern day user, with the increase in device intercommunication, is fuelling the latest Internet of Things (IoT) development. IoT needs to enable exchange of various types of data, from sensor data to multimedia, between numerous diverse devices differing in power, connectivity, mobility and energy, while also maintaining high levels of Quality of Service (QoS). This paper performs statistical analysis of the innovative NETworking Scheme for sMart IoT gatewayS (NETSMITS) in terms of several QoS network-related metrics with most significant impact on devices' performance. NETSMITS introduces an innovative algorithm which uses QoS and service relevance metrics in order to efficiently cluster intercommunicating IoT objects. Statistical analysis is performed on the QoS data collected in a highly relevant multi-device scenario in order to understand NETSMITS' behaviour. Interesting results were obtained, describing the relationship between the QoS metrics and different types of IoT devices.

In recent years, the emerging immersive technologies (e.g. Virtual/Augmented Reality, multisensorial media) bring brand-new multi-dimensional effects such as 3D vision, immersion, vibration, smell, airflow, etc. to gaming, video entertainment and other aspects of human life. This paper reports results from an European Horizon 2020 research project on the impact of multisensoral media (mulsemedia) on educational learner experience. A mulsemedia-enhanced test-bed was developed to perform delivery of video content enhanced with haptic, olfaction and airflow effects. The results of the quality rating and questionnaires show significant improvements in terms of mulsemedia-enhanced teaching.

Recently, the concept of adding multisensory media components to complement and extend user Quality of Experience (QoE) of traditional media has gained attention from both academia and industry. Research works stimulating additional senses like olfaction (sense of smell), haptic (sense of touch) and gustation (sense of taste) have emerged. In particular in theme parks, multisensory experiences that also offer ambient lighting effects, vibrating seats, wind generators, mist effects, heaters/coolers, etc. are appearing. Considering this growing awareness and popularity, a key research challenge is to experimentally evaluate if and how these different effects affect user QoE. In this context, there is a lack of common test content and raw data results to support reproducible research and cross research team verification. This paper fills this gap. We share: the data from the empirical study; the video content; the olfactory components employed to enrich the video; the methodologies employed and various other aspects found through experience to be important. Uniquely, this work is complemented by two datasets, obtained in two separate but related empirical studies, one conducted in the UK, and the other in Ireland.

There are interdependent increases in vehicle numbers, vehicular traffic congestion, and carbon emissions that cause major problems worldwide. These problems include direct negative influences on people's health, adverse economic effects, negative social impacts, local environmental damage, and risk of catastrophic global climate change. There is a drastic need to develop ways to reduce these emissions and EcoTrec, presented in this paper, is one of these innovative approaches. EcoTrec is a vehicular ad hoc network-based vehicle routing solution designed to reduce vehicle carbon emissions without significantly affecting the travel times of vehicles. The vehicles exchange messages related to traffic and road conditions, such as average speed on the road, road gradient, and surface condition. This information is used to build a fuel efficiency model of the routes, based on which the vehicles are recommended to take more efficient routes. By routing vehicles more efficiently, the greenhouse emissions are reduced while also maintaining low traffic congestion levels. This paper presents results of extensive simulations, which show how EcoTrec outperforms other state-of-the-art solutions with different number of vehicles, vehicle penetration, and compliance rates, and when considering different real world road maps from Dublin and Koln.

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-data centers meant to deal with this explosion of data traffic. However, this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators' revenue. In this context, there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for data center networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity.

In the quest to increase user perceived quality of experience (QoE), the classic audio-visual content paradigm can be extended to include media components that stimulate other human senses. Among these, olfaction-enhanced multimedia has attracted significant attention, as it is both attractive from user point of view and challenging from research perspective. This paper presents the results of two subjective studies which analyzed user QoE of olfaction-enhanced multimedia. Diverse scent types and video content were considered. In particular, QoE levels were studied when one and two olfaction stimuli enhanced audiovisual media. The results presented show that scent type influences user QoE. Statistically significant differences between pleasant and unpleasant scent types existed. Also, in certain cases, users were prepared to forgive the presence of unpleasant scent types with respect to QoE. Finally, users reported a clear preference for olfaction presented after the video sequence with which the olfaction effect should be synchronized, as opposed to before the video sequence.

With the aim to enrich users' perceived multimedia experience, the authors present the results of an empirical study which looked at user perception of olfaction based mulsemedia. The goal is to evaluate the influence of users' age and gender on user quality of experience (QoE) considering various scent types and categories (pleasant or not). The results present a complex relationship between these variables and how they influence user QoE. They indicate that different user groups report different perception of content level factors for olfaction based mulsemedia.

The continuous growth in user demand for high-quality rich media services puts pressure on Wireless Mesh Network (WMN) resources. Solutions such as those which increase the capacity of the mesh network by equipping mesh routers with additional wireless interfaces provide better Quality of Service (QoS) for video deliveries, but result in higher overall energy consumption for the network. This paper presents LBIS, a distributed solution which combines the benefits of both load-balancing and interface-shifting in order to enhance QoS levels for video services delivered over multi-hop WMNs, while maintaining low energy consumption levels within the network. Simulation-based results show very good performance of our proposed mechanism in terms of QoS metrics (delay, packet loss), Peak Signal-to-Noise Ratio (PSNR) and energy consumption in mesh network topologies, and with varying video traffic loads and distributions. The results demonstrate how LBIS can increase the QoS for video deliveries by more than 30% at the cost of an insignificant increase of the overall network energy consumption compared to the WMN with multiple radio interfaces without the LBIS adaptation.

Media-rich content plays a vital role in consumer applications today, as these applications try to find new and interesting ways to engage their users. Video, audio, and the more traditional forms of media content continue to dominate with respect to the use of media content to enhance the user experience. Tactile interactivity has also now become widely popular in modern computing applications, while our olfactory and gustatory senses continue to have a limited role. However, in recent times, there have been significant advancements regarding the use of olfactory media content (i.e., smell), and there are a variety of devices now available to enable its computer-controlled emission. This paper explores the impact of the audio stream on user perception of olfactory-enhanced video content in the presence of skews between the olfactory and video media. This research uses the results from two experimental studies of user-perceived quality of olfactory-enhanced multimedia, where audio was present and absent, respectively. Specifically, the paper shows that the user Quality of Experience (QoE) is generally higher in the absence of audio for nearly perfect synchronized olfactory-enhanced multimedia presentations (i.e., an olfactory media skew of between {−10,+10s}); however, for greater olfactory media skews (ranging between {−30s;−10s} and {+10s, +30s}) user QoE is higher when the audio stream is present. It can be concluded that the presence of the audio has the ability to mask larger synchronization skews between the other media components in olfaction-enhanced multimedia presentations.

Recently, mobile multimedia services have represented an increasingly large source of revenue for the telecommunication industry. Subscribers are often interested in simultaneously receiving the same data flow and, hence, they fuel the growing demand for multicast multimedia services. Therefore, the design of efficient radio resource management strategies to jointly handle multicast and more traditional unicast traffic and to increase user satisfaction is of primary importance for the successful deployment of future mobile networks. This paper proposes an efficient radio resource management framework to handle unicast and multicast multi-layer video services. The proposed virtual unified group (VUG) approach makes use of a channel-aware subgrouping principle to provide fair throughput to both unicast and multicast users. The idea is to assign unicast subscribers to a virtual group, thus allowing them to compete for network resources on an equal footing with multicast users. Simulations highlight the benefits that VUG provides in a long term evolution network scenario, under different traffic load conditions, in terms of throughput and fairness in the distribution of resources to unicast and multicast users.

With the technological evolution in telecommunication networks, performance requirements such as better coverage, higher bandwidth, and lower latency have been pushed to new horizons. However, as a direct result network complexity has increased dramatically over the recent years, and with this complexity manageability has suffered. This paper presents the architecture of the E-Stream project which aims to support Next Generation Operations Support Systems. E-Stream applies dimension reduction, data mining, and recommender system techniques in order to handle very high volumes of management events, identify and predict network incidents, and recommend candidate corrective actions to domain experts in Network Operations Centres.

Currently the volume of telecom network management data is expanding exponentially, mainly due to the explosive growth in the number of communicating devices along with the increase in heterogeneity of the networks. Such scale of data obsoletes the traditional approach of extracting offline analytics from the network traces governed by some pre-defined schemes. In order to increase the efficiency of the Operations Support System (OSS) and gain in-depth understanding of the generic relationship between network entities, the monitoring data needs to undergo large-scale deep analytics processing. In this paper we present i-MagNet, an integrated analytics framework developed with the popular real-time stream processing paradigm Storm. The components of i-MagNet intelligently micro-batch segments of incoming streams to enable high-throughput online analytics of management trace streams. Inter-dependence metrics (temporal and statistical) are exploited to extract contiguous event subsequences, which can then be independently examined as part of a network incident analysis system.

One of the factors influencing the quality of a mobile user's multimedia experience is the rate at which they can receive data. To connect to the wireless network that best meets their needs a user must first detect available networks and then select the most suitable network. However, it may not always be in the best interest of the user to actually invoke a network detection and selection strategy. Simulation results show that in certain conditions it is detrimental to the end user to switch networks, even when an apparently `better' network is detected. In order to help decide when it is appropriate to invoke network detection and selection algorithms, this paper introduces the Scan-Or-Not-to-Scan (SONS) framework. SONS decides based on environmental inputs, when to invoke or not a network detection and selection algorithm. The use of the SONS framework enables the user to conserve energy by shutting down unused interfaces and maximise data throughput. Reducing the number of unnecessary handovers helps maintain the mobile user multimedia quality of experience.

Handover algorithms typically operate by assigning preconfigured threshold or weight values onto network performance metrics such as delay, data loss and signal strength. Such approaches are performance limited as they do not consider external factors that affect the network such as the physical environment and current weather conditions. Previous research illustrates that foliage density combined with detrimental weather conditions can have degrading effects on wireless links. The changes to these environmental factors over long vehicular-based mobile user sessions can lead to sub-optimal handover decisions and a negative impact on a user's Quality of Experience during mobile video streaming. There is need for a handover approach that adapts to these factors and mitigates any negative effects that occur. This paper proposes a method for Environmental factor Mitigation on mULtimedia StreamIng Networks (EMULSIoN). EMULSIoN uses a perceptron artificial neural network approach to mitigate the latency and delays caused by environmental factors. Using dynamic network performance metrics and with known topographical data, the EMULSIoN directed learning approach can learn from previous user sessions to mitigate these environmental effects. EMULSIoN further uses GPS and topographical data to divide vehicular routes into small sub-areas for optimal performance in varied terrain. Results illustrate that EMULSIoN has significant video quality improvements in comparison to pre-configured weight handover strategies.

The growing availability of various wireless access technologies promotes increasing demand for mobile video applications. Stream control transmission protocol (SCTP)-based concurrent multipath transfer (CMT) improves the wireless video delivery performance with its parallel transmission and bandwidth (BW) aggregation features. However, the existing CMT solutions deployed at the transport layer only are not accurate enough due to lower layer uncertainties, such as variations of the wireless channel. In addition, CMT-based video transmission may use excessive BW in comparison with the popular Transmission Control Protocol (TCP)-based flows, which results in unfair sharing of network resources. This paper proposes a novel cross-layer fairness-driven (CL/FD) SCTP-based CMT solution (CMT-CL/FD) to improve video delivery performance, while remaining fair to the competing TCP flows. CMT-CL/FD utilizes a cross-layer approach to monitor and analyze path quality, which includes wireless channel measurements at the data-link layer and rate/BW estimations at the transport layer. Furthermore, an innovative window-based mechanism is applied for flow control to balance delivery fairness and efficiency. Finally, CMT-CL/FD intelligently distributes video data over different paths depending on their estimated quality to mitigate packet reordering and loss, under the constraint of TCP-friendly flow control. Simulation results show how CMT-CL/FD outperforms existing solutions in terms of both video delivery performance and TCP-friendliness.

Multimedia synchronization involves a temporal relationship between audio and visual media components. The presentation of “in-sync” data streams is essential to achieve a natural impression, as “out-of-sync” effects are often associated with user quality of experience (QoE) decrease . Recently , multi-sensory media (mulsemedia) has been demonstrated to provide a highly immersive experience for its users. Unlike traditional multimedia, mulsemedia consists of other media types (i.e., haptic, olfaction, taste, etc.) in addition to audio and visual content. Therefore, the goal of achieving high quality mulsemedia transmission is to present no or little synchronization errors between the multiple media components. In order to achieve this ideal synchronization, there is a need for comprehensive knowledge of the synchronization requirements at the user interface. This paper presents the results of a subjective study carried out to explore the temporal boundaries within which haptic and air-flow media objects can be successfully synchronized with video media. Results show that skews between sensorial media and multimedia might still give the effect that the mulsemedia sequence is “in-sync” and provide certain constraints under which synchronization errors might be tolerated. The outcomes of the paper are used to provide recommendations for mulsemedia service providers in order for their services to be associated with acceptable user experience levels, e.g. haptic media could be presented with a delay of up to 1 s behind video content, while air-flow media could be released either 5 s ahead of or 3 s behind video content.

Multiple sensorial media (mulsemedia) combines multiple media elements which engage three or more of human senses, and as most other media content, requires support for delivery over the existing networks. This paper proposes an adaptive mulsemedia framework (ADAMS) for delivering scalable video and sensorial data to users. Unlike existing two-dimensional joint source-channel adaptation solutions for video streaming, the ADAMS framework includes three joint adaptation dimensions: video source, sensorial source, and network optimization. Using an MPEG-7 description scheme, ADAMS recommends the integration of multiple sensorial effects (i.e., haptic, olfaction, air motion, etc.) as metadata into multimedia streams. ADAMS design includes both coarse- and fine-grained adaptation modules on the server side: mulsemedia flow adaptation and packet priority scheduling. Feedback from subjective quality evaluation and network conditions is used to develop the two modules. Subjective evaluation investigated users' enjoyment levels when exposed to mulsemedia and multimedia sequences, respectively and to study users' preference levels of some sensorial effects in the context of mulsemedia sequences with video components at different quality levels. Results of the subjective study inform guidelines for an adaptive strategy that selects the optimal combination for video segments and sensorial data for a given bandwidth constraint and user requirement. User perceptual tests show how ADAMS outperforms existing multimedia delivery solutions in terms of both user perceived quality and user enjoyment during adaptive streaming of various mulsemedia content. In doing so, it highlights the case for tailored, adaptive mulsemedia delivery over traditional multimedia adaptive transport mechanisms.

Multipath TCP (MPTCP) is an evolution of the regular TCP that allows multiple radio interfaces to be used simultaneously by a single connection while presenting regular TCP interface to applications. Although its benefits include better resource utilization, higher throughput and smoother reaction to connection failures, MPTCP does not take energy consumption into account, especially important when using wireless mobile devices with limited power resources. In this paper, we demonstrate that smartphones with MPTCP support consume more energy than those with regular TCP when using the same service and the same network interface. Additionally, novel energy consumption models are developed based on real life measurements on a real life smartphone. The proposed energy consumption models consider four different multimedia-based services (i.e. video streaming, voice over IP, web-browsing and file download) in 3G and WiFi networks when MPTCP or regular TCP are used, respectively.

Increasing amounts of time is wasted due to traffic congestion in both developed and developing countries. This has severe negative effects, including drivers stress due to increased time pressure, reduced usage efficiency of trucks and other commercial vehicles, and increased gas emissions-responsible for climate change and air pollution affecting population health in densely populated areas. As existing centralized approaches were neither efficient, nor scalable, there is a need for alternative approaches. Social insects provide many solutions for dealing with decentralized problems. For instance ants choose their routes based on pheromones left by previous ants. However, Ant Colony Optimization is not directly applicable to vehicle routing, as routing the vehicles to the same road would cause traffic congestion. Yet, the traffic is broadly similar from work-to work-day. This paper introduces an ant-colony optimization-based algorithm called Time-Ants. Time-Ants considers that an amount of “pheromone” or a traffic rating is assigned to each road at any given time in the day. Using an innovative algorithm the vehicle's routes are chosen based on these traffic ratings, aggregated in time. After several iterations this results in a global optimum for the traffic system. Bottlenecks are identified and avoided by machine learning. Time-Ants outperforms another leading algorithm by up to 19% in terms of percentage of vehicles to reach the destination within a given time-frame.

Most cities have special lanes dedicated to buses, however these lanes are rarely used at full capacity. At the same time governments around the world are encouraging people to buy electric vehicles. This paper proposes the creation of electric vehicle enhanced dedicated bus lanes (E-DBL), by allowing electric vehicles access to bus lanes, in order to improve the use of road capacity. By opening bus lanes to electric vehicles, traffic congestion could be eased, the range of electric vehicles could be extended, and the travel times for electric vehicle owners could be reduced significantly. The paper shows how by introducing E-DBLs, the bus journey times are not significantly affected given the current uptake of electric vehicles in most developed countries. This paper presents extensive simulations based on traffic situation in the city of Dublin with regard to the effect of opening up bus lanes to electric vehicles. The results show that even with very high percentages of electric vehicles the bus journey times are not noticeably affected. Opening up bus lanes to electric vehicles can even be beneficial for other road users by reducing congestion on regular lanes, which would further reduce carbon emissions.

Energy consumption is a key issue that impacts on user quality of experience when delivering rich media services to smartphones via heterogeneous wireless networks. Previous research works have studied the smartphone energy consumption in a broad manner only. This paper focuses on comparative energy consumption investigation of rich media transmissions over 3G and WiFi networks involving a real life smartphone device. In particular, the energy consumption of the CPU and radio interfaces (i.e. HSDPA and WiFi) for different rich media services (i.e. video streaming, interactive video call, file download, web-browsing) is recorded. The results obtained show how deliveries over the WiFi interface are more energy efficient than those over the 3G interface (i.e. up to 36.5% for downloading service). Additionally, the difference between the energy consumption when employing WiFi and 3G is the lowest and highest for web-browsing and file downloading services, respectively. Outcome of the investigation can provide beneficial input for smartphone energy optimization solutions.

Traditional video sequences make use of both visual images and audio tracks which are perceived by human eyes and ears, respectively. In order to present better ultra-reality virtual experience, the comprehensive human sensations (e.g. olfaction, haptic, gustatory, etc) needed to be exploited. In this paper, a multiple sensorial media (mulsemedia) delivery system is introduced to deliver multimedia sequences integrated with multiple media components which engage three or more of human senses such as sight, hearing, olfaction, haptic, gustatory, etc. Three sensorial effects (i.e. haptic, olfaction, and air-flowing) are selected for the purpose of demonstration. Subjective test is conducted to analyze the user perceived quality of experience of the mulsemedia service. It is concluded that the mulsemedia sequences can partly mask the decreased movie quality. Additionally the most preferable sensorial effect is haptic, followed by air-flowing and olfaction.

Monitoring and management of modern telecommunication networks has become more and more challenging due to the explosion in scale of data generated by network elements. Not only has the size of the network, the number of nodes, and the number of customers increased, but the amount and dimensionality of the data coming from each managed element has also increased. To support sophisticated monitoring and management strategies it is desirable to forward as much trace data as possible into operators' operations support systems (Operations Support Systems (OSSs)). In this paper a heuristic algorithm is presented which reduces the data-stream by removing uncorrelated noise events by determining the degree of inter-relationship between the events in the data-stream. With a sophisticated open source control plane emulator used as the source generator, the results show that the presented algorithm is capable of differentiating noise from useful information thus significantly reducing scale and dimensionality of network monitoring data-streams.

Concurrent Multipath Transfer (CMT), enabled by Stream Control Transmission Protocol (SCTP), is considered as one preferred data-transport mode due to increased available bandwidth. However, CMT performance degrades seriously in terms of data reordering due to path dissimilarity and frequent packet loss from wireless unreliability. Most relevant solutions follow the packet sequence numbers and thereby focus on strict in-order reception and packet-specific retransmission. Passively adapting to the network conditions, those approaches are not general and well enough responding to the dynamicity of wireless environment. By applying Network Coding (NC) to CMT, this paper proposes a progressive end-to-end solution (CMT-NC) to those problems in heterogeneous wireless networks. CMT-NC is capable of avoiding reordering and compensating lost packets. Further, an innovative group-based transmission management mechanism enhances the robustness and reliability of data transfer. Simulation results show how by using CMT-NC significant improvements in comparison to another state-of-the-art solution are obtained.

Mobile devices became an essential part of every person daily routine enabling them to browse the Internet, watch videos, work and play online anytime and anywhere. However this led to a tremendous growth in user generated data traffic putting significant pressure on the underling network technology. Thus, in order to cope with this explosion of data traffic, Wi-Fi offload became a popular solution for network operators. The solution enables the network operators to accommodate more mobile users and keep up with their traffic demands. Moreover, with the energy conservation becoming a critical issue around the world, it provides motivation for this paper to propose an Energy-aware Device-Oriented Adaptive multimedia Scheme (eDOAS) for Wi-Fi Data Offload. eDOAS adapts the interactive multimedia application to the underlying Wi-Fi network conditions, device characteristics and device energy consumption, in order to prolong the battery lifetime of the mobile device and maintain an acceptable user perceived quality level. Real test-bed energy consumption measurements were conducted on five different devices and the performance of eDOAS was analyzed against other schemes from the literature, in terms of energy consumption, service outage, average throughput, packet loss and PSNR.

The reduction of energy consumption is a major concern in the current telecommunications environment - especially with the growth in usage of energy-hungry multimedia-centric applications on high-end mobile devices. In this context, this paper proposes eSMART, an Energy-efficient Scalable Multimedia Broadcast Transmission mechanism, that considers the energy-quality trade-off to reduce battery power consumption (increase energy saving) of heterogeneous mobile devices while maintaining acceptable perceived quality levels of received video. A real experimental test-bed has been built to analyze the impact of different multimedia scalability factors on the energy consumption of various mobile devices receiving broadcast content. Overall mobile device energy-saving is modeled using the accumulative effect of adaptive scalable video playback energy saving and time-sliced broadcast reception based radio-receiver's energy saving. eSMART's optimization framework performs user-centric adaptive encoding of scalable video that is broadcast to heterogeneous user equipments. eSMART serves more users at improved quality of experience levels and achieves up to 69% increase in mobile device energy savings as compared to a non-adaptive time-slicing scheme from the literature.

This paper presents a novel cross-layer optimization framework to improve the quality of user experience (QoE) and energy efficiency of the heterogeneous wireless multimedia broadcast receivers. This joint optimization is achieved by grouping the users based on their device capabilities and estimated channel conditions experienced by them and broadcasting adaptive content to these groups. The adaptive multimedia content is obtained by using scalable video coding (SVC) with optimal source encoding parameters resulted from an innovative cooperative game. Energy saving at user terminals results from using a layer-aware time slicing approach in the transmission stage. A trade-off between energy saving and QoE is observed, and is incorporated in the definition of a utility function of the players in the formulated heterogeneous user composition and physical channel aware game. An adaptive modulation and coding scheme is also optimally incorporated in order to maximize the reception quality of the broadcast receivers, while maximizing the network broadcast capacity. Compared to the conventional broadcast schemes, the proposed framework shows an appreciable improvement in QoE levels for all users, while achieving higher energy-savings for the energy constrained users.

This study looked at users' perception of interstream synchronization between audiovisual media and two olfactory streams. The ability to detect skews and the perception and impact of skews on user Quality of Experience (QoE) is analyzed. The olfactory streams are presented with the same skews (i.e., delay) and with variable skews (i.e., jitter and mix of scents). This article reports the limits beyond which desynchronization reduces user-perceived quality levels. Also, a minimum gap between the presentations of consecutive scents is identified, necessary to ensuring enhanced user-perceived quality. There is no evidence (not considering scent type) that overlapping or mixing of scents increases user QoE levels for olfaction-enhanced multimedia.

As a step towards enhancing users' perceived multimedia quality levels, this article presents the results of a study which looked at user's perception of inter-stream synchronization between scent and video. The ability to detect and the perception of and impact of skew on user's quality of experience is analyzed considering user's age, sex, and culture (user profile). The results indicate that skews beyond a certain level between olfaction and video have a negative impact on user-perceived experience. Olfaction before video is more noticeable to users than olfaction after video, and assessors are more tolerable of olfactory data presented after video.

User Quality of Experience (QoE) is of fundamental importance in multimedia applications and has been extensively studied for decades. However, user QoE in the context of the emerging multiple-sensorial media (mulsemedia) services, which involve different media components than the traditional multimedia applications, have not been comprehensively studied. This article presents the results of subjective tests which have investigated user perception of mulsemedia content. In particular, the impact of intensity of certain mulsemedia components including haptic and airflow on user-perceived experience are studied. Results demonstrate that by making use of mulsemedia the overall user enjoyment levels increased by up to 77%.

Wireless Mesh Networks (WMNs) are becoming increasingly popular and user demand for high-quality rich media services is continuously growing. Despite the fact that WMNs offer significant flexibility, they suffer in respect to Quality of Service (QoS) provisioning. This paper proposes a novel mechanism for providing enhanced QoS support to video services in multi-hop WMNs. The mechanism makes use of an innovative hybrid hierarchical architecture which combines centralized and distributed approaches. The proposed solution relies on performance monitoring at WMN nodes and performs load balancing by off-loading traffic from the highest loaded nodes to less loaded neighbours. Simulation-based results presented outline the performance of our proposed mechanism in terms of QoS metrics (delay, throughput, packet losses and PSNR) in different network load scenarios. The results clearly demonstrate how our proposed mechanism outperforms the traditional OLSR protocol in terms of QoS performance.

Being able to react fast to campaign events such as missing persons or disaster preventions, is of paramount importance. In these situations narrowing down the search area to a targeted and accurate location is imperative. Nowadays, modern mobile devices have the location awareness capabilities that can be used to determine the users Global Positioning System (GPS) coordinates. However in order to determine if a user is located within a specific area, complex floating point calculations are required. Moreover if the area is determined by a polygon, this calculation is further complicated. In this paper we propose a novel algorithm which makes use of spatial indices to determine if a mobile is located within a predefined polygon shape area. The algorithm determines the optimal length of the spatial index such as to ensure accuracy-processing time-memory trade-off. We build a prototype system, using free and open source software, to deliver alerts to mobile devices within a predetermined geographical area. The system is assessed in terms of accuracy, processing time and memory usage.

Lately, significant efforts have being put into proposing various solutions for increasing multimedia viewers' perceived quality levels. One innovative avenue is to enhance users' quality of experience (QoE) by extending the classic audio-visual multimedia content to stimulate also other human senses such as olfaction, tactile, etc. In this context, this paper focuses on olfaction-enhanced multimedia content and presents the results of an experimental study which looked at user perception of inter-stream synchronization between olfactory data and video, whereby the audio used provides no contextual information. The study investigates how age and gender influence users' perception of the temporal boundaries within which they perceive olfactory data and video to be synchronized. The impact on user QoE levels (considering sense of enjoyment, relevance and reality) during synchronous and asynchronous presentations of olfactory and video media is also analyzed and discussed. The results show that there are significant differences in terms of how users of various gender and age groups perceive the skew between olfaction and video content and in their QoE levels.

In the current telecommunication environment, network operators are trying to cope with a significant increase in data traffic by adopting different solutions to expand their network capacity. One of these solutions is the convergence of next generation wireless networks (e.g., HSDPA, LTE and WiMAX) which involve closely interworking of existing 2G/2.5G/3G networks with the new next generation networks in terms of handover and network selection. However, the diversification in mobile devices and the heterogeneity of the wireless environment make the seamless always best connectivity of mobile users a challenge for the service providers. We propose RLoad, a novel Reputation-based Load-balancing Network Selection Strategy for heterogeneous wireless environments, built on top of the IEEE 802.21 Media Independent Handover (MIH) standard. The proposed solution makes use of a reputation-based mechanism to select the most appropriate set of networks for the mobile user and a load balancing mechanism to distribute the traffic load among the networks by making use of the Multipath TCP (MPTCP) protocol. Preliminary simulation results show significant benefits when using the proposed RLoad solution.

Cycling, as a very attractive green form of transportation, is also one of the most sustainable. Electric bicycles, the most popular electric vehicles, subscribe to this type of transportation, being highly environmentally friendly. They have several advantages when compared to traditional bicycles, but also a weak point in terms of long battery (re)charging duration. Consequently power-saving solutions for electric bicycles are of high research interest. In this context, this paper proposes a novel energy-efficient weather-aware route planner (eWARPE) for electric bicycles. The solution makes use of the weather information in order to recommend the optimal departure time that allows the cyclist to avoid the adverse weather conditions and to maximize the energy savings of the electric bicycle. Note that the departure time is in a user-configurable time interval. The departure time can be recommended for a preferred route introduced by the user or for a route built in eWARPE based on user input. The proposed solution was validated through numerical analysis. Moreover, a survey was conducted in order to assess the impact of the adverse weather conditions on cyclists and to measure how the cyclists will benefit from the proposed solution.

This paper introduces the Slow sTart Exponential and Linear Algorithm (STELA), a novel transceiver duty cycle management strategy which intelligently adapts the sleeping schedule of mobile terminal radio interfaces during multimedia data transfer over wireless networks in order to reduce energy consumption while maintaining high delivery performance. The paper presents the algorithm, a simulation-based model and performance evaluation in terms of energy consumption and network Quality of Service (QoS) parameters. The proposed algorithm, STELA, is compared with similar algorithms used by IEEE 802.11 and IEEE 802.16 MAC standard protocols. Experimental testing results demonstrate that in the context of various Constant Bit-Rate (CBR) and Variable Bit-Rate (VBR) traffic patterns, typical for multimedia content delivery, STELA reduces the energy used by the mobile device for wireless communication with up to 55%, while maintaining good network QoS levels.

As a step towards enhancing users' perceived multimedia quality levels beyond the level offered by the classic audiovisual systems, the authors present the results of an experimental study which looked at user's perception of inter-stream synchronization between olfactory data (scent) and video (without relevant audio). The impact on user's quality of experience (by considering enjoyment, relevance and reality) comparing synchronous with asynchronous presentation of olfactory and video media is analyzed and discussed. The aim is to empirically define the temporal boundaries within which users perceive olfactory data and video to be synchronized. The key analysis compares the user detection and perception of synchronization error. State of the art works have investigated temporal boundaries for olfactory data with audiovisual media, but no works document the integration of olfactory data and video (with no related audio). The results of this work show that the temporal boundaries for olfactory and video only are significantly different from olfactory, video and audio. The authors conclude that the absence of contextual audio reduces considerably the acceptable temporal boundary between the scent and video. The results also indicate that olfaction before video is more noticeable to users than olfaction after video and that users are more tolerable of olfactory data after video rather than olfactory data before video. In addition the results show the presence of two main synchronization regions. This work is a step towards the definition of synchronization specifications for multimedia applications based on olfactory and video media.

The growing popularity of the high-end mobile computing devices - smartphones, tablets, notebooks and more - equipped with high-speed network access, enables the mobile user to watch multimedia content from any source on any screen, at any time, while on the move or stationary. In this context, the network operators must ensure smooth video streaming with the lowest service delay, jitter, and packet loss. This paper proposes a resource efficient Device-Oriented Adaptive Multimedia Scheme (DOAS) built on top of the downlink scheduler in LTE-Advanced systems. DOAS bases its adaptation decision on the end-user device display resolution information and Quality of Service (QoS). DOAS is implemented on top of the Proportional Fair (PF) and the well-known Modified Largest Weighted Delay First (M-LWDF) scheduling algorithms within the 3GPP LTE/LTE-Advanced system. The performance of the proposed adaptive multimedia scheme was analyzed and compared against a non-adaptive solution in terms of throughput, packet loss and PSNR.

In wireless mesh networks, mesh devices often have limited power budgets while performing complex and energy-consuming application tasks such as multimedia deliveries. In this context, reducing energy consumption is one of the main research concerns, yet most of the existing multimedia delivery schemes proposed for wireless mesh networks do not consider this factor. This paper presents AOC-MAC, an adaptive MAC-layer operation cycle management solution for high-quality multimedia transmissions over wireless mesh networks. AOC-MAC saves energy at mesh network devices by managing their sleep-periods in an innovative way while also maintaining high multimedia quality levels. AOC-MAC is deployed in conjunction with E-Mesh, an energy-aware routing mechanism, as part of an energy-aware cross-layer scheme. Network Simulator 3 (NS-3) simulation results show the balancing effect of obtaining energy savings and maintaining good multimedia quality levels by using AOC-MAC in comparison with the case when the standard IEEE 802.11s protocol is deployed.

The lack of significant breakthroughs in terms of alternative energy sources has caused both fuel consumption and gas emissions to constantly increase. In this context, improving fuel efficiency and reducing emissions in the transportation sector is vital, as vehicles are one of the important contributors to air pollution. This paper introduces EcoTrec, a novel eco-friendly routing algorithm for vehicular traffic which considers road characteristics such as surface conditions and gradients, as well as existing traffic conditions to improve the fuel savings of vehicles and reduce gas emissions. EcoTrec makes use of the Vehicular Ad-hoc NETworks (VANET) both for collecting data from distributed vehicles and to disseminate information in aid of the routing algorithm. The algorithm calculates the fuel efficiency of various routes and then directs the vehicle to a fuel efficient route, while also avoiding flash crowding. Simulation-based tests showed that by using EcoTrec, fuel emissions were significantly reduced, when compared with existing state-of-the-art vehicular routing algorithms.

Vehicular ad-hoc networks (VANETs) are considered to have an enormous potential in enhancing road traffic safety and traffic efficiency. Socio-economic challenges, network scalability and stability are identified among the main challenges in VANETs. In response to these challenges, this paper proposes a novel user-oriented Fuzzy Logic-based k-hop distributed clustering scheme for VANETs that takes into consideration the vehicle passenger preferences. The novelty element introduced is the employment of Fuzzy Logic as a prominent player in the clustering scheme. To the best knowledge of the authors, there are no Fuzzy Logic-based clustering algorithms designed for VANETs. Simulation-based testing demonstrate how the proposed solution increases the stability of vehicular networks, lifetime and stability of cluster heads compared to both the classic Lowest ID algorithm and an utility function-based clustering scheme previously proposed by the same authors.

IETF-proposed Multipath TCP (MPTCP) extends the standard TCP and allows data streams to be delivered across multiple simultaneous connections and consequently paths. The multipath capability of MPTCP provides increased bandwidth for applications in comparison with the classic single-path TCP, which makes it highly attractive for the current consumer mobile devices that support more than one radio interfaces (e.g. 3G, WiFi, Bluetooth, etc.). However, MPTCP does not consider energy consumption aspects which are highly important for these devices. This paper proposes eMTCP, a novel energy-aware MPTCP-based content delivery scheme which balances support for increased throughput with energy consumption awareness. eMTCP is located at upper transport layer in mobile devices and requires no additional modifications of the MPTCP-enabled server. eMTCP increases the energy efficiency of mobile devices by offloading traffic from the more energy-consuming interfaces to others. Simulation-based experiments employing an eMTCP model which sends data streams via the 3GPP Long Term Evolution (LTE) and IEEE 802.11 (WiFi) interfaces show an increase of up to 14% in energy efficiency when using eMTCP in comparison with MPTCP and of up to 66% in terms of quality in comparison with single-path TCP.

The limited battery capacity of current mobile devices and increasing amount of rich media content delivered over wireless networks have driven the latest research on energy efficient content delivery over wireless networks. Many energy-aware research solutions have been proposed involving traffic shaping, content adaptation, content sharing, etc. The existing solutions focus on the delivery application without considering application running environment and device features that pose different energy constraints on the whole content delivery process. This paper presents a Device characteristics-based differentiated Energy-efficient Adaptive Solution (DEAS) for video delivery over heterogeneous wireless networks. DEAS constructs an energy-oriented system profile including power signatures of various device components for each running application. Based on this profile, an energy efficient content delivery adaptation is performed for the current application. The proposed solution is evaluated by simulation-based testing and compared with other state of the art approaches in terms of performance and energy efficiency. The results show how DEAS outperforms the other well-known solutions.

Mobile devices equipped with multiple network interfaces can increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation, enabled by the stream control transport protocol (SCTP). However, the different bandwidth and delay of the multiple paths will determine data to be received out of order and in the absence of related mechanisms to correct this, serious application-level performance degradations will occur. This paper proposes a novel quality-aware adaptive concurrent multipath transfer solution (CMT-QA) that utilizes SCTP for FTP-like data transmission and real-time video delivery in wireless heterogeneous networks. CMT-QA monitors and analyses regularly each path's data handling capability and makes data delivery adaptation decisions to select the qualified paths for concurrent data transfer. CMT-QA includes a series of mechanisms to distribute data chunks over multiple paths intelligently and control the data traffic rate of each path independently. CMT-QA's goal is to mitigate the out-of-order data reception by reducing the reordering delay and unnecessary fast retransmissions. CMT-QA can effectively differentiate between different types of packet loss to avoid unreasonable congestion window adjustments for retransmissions. Simulations show how CMT-QA outperforms existing solutions in terms of performance and quality of service.

IEEE 802.11e protocol enables QoS differentiation between different traffic types, but requires MAC layer support and assigns traffic with static priority. This paper proposes an intelligent Prioritized Adaptive Scheme (iPAS) to provide QoS differentiation for heterogeneous multimedia delivery over wireless networks. iPAS assigns dynamic priorities to various streams and determines their bandwidth share by employing a probabilistic approach-which makes use of stereotypes. Unlike existing QoS differentiation solutions, the priority level of individual streams in iPAS is variable and considers service types and network delivery QoS parameters (i.e. delay, jitter, and packet loss rate). A bandwidth estimation technique is adopted to provide network conditions and the IEEE 802.21 framework is used to enable control information exchange between network components without modifying existing MAC protocol. Simulations and real life tests demonstrate how better results are obtained when employing iPAS than when either IEEE 802.11 DCF or 802.11e EDCA mechanisms are used. The iPAS key performance benefits are as follows: 1) better fairness in bandwidth allocation; 2) higher throughput than 802.11 DCF and 802.11e EDCA with up to 38% and 20%, respectively; 3) enables definite throughput and delay differentiation between streams.

Information provided in real-time via Vehicular Ad-hoc Networks is of great value in any kind of traffic systems. Bandwidth issues arise in this type of networks due to the potential large number of nodes. Data aggregation addresses these issues avoiding the dissemination of similar messages in the network. The lack of flexibility in the similarity criteria, security issues and the need of standardization were mentioned among the challenges of data aggregation schemes. Fuzzy Logic, very efficient in real-time systems, has been lately employed in data aggregation schemes. This paper analyzes various solutions for using Fuzzy Logic in data aggregation schemes and their mode of addressing the underlined challenges. The analysis conducted concludes that making use of Fuzzy Logic in data aggregation schemes is suitable to solving some of their issues and has great benefits in the development process of traffic systems that relies on these schemes.

Smart phones have gained great popularity all around the world, supporting rich media applications with Internet connectivity. Since increasing number of people own powerful mobile devices, ad-hoc WLANs can be deployed in public areas part of heterogeneous networks environments, as flexible and inexpensive alternatives to infrastructure-based approaches. However, these mobile devices are powered by battery with limited energy budgets, which introduces big energy-related challenges in ad-hoc WLAN routing algorithm design. This paper proposes a context-aware cross-layer energy-efficient adaptive routing algorithm for WLAN communications (AWERA) that performs energy-efficient context differentiated routing in wireless communications. It introduces a cross layer self-learning solution that monitors the context of device usage, and takes routing decisions based on current energy-oriented context. Compared with other state-of-the-art wireless routing protocols, simulation results show both better performance and energy efficiency context-based differentiation.

This paper introduces a novel Medium Access Control (MAC) strategy for energy efficient wireless data transmissions which reduces energy consumption while minimizing the negative impact on network Quality of Service (QoS). This strategy adaptively adjusts the sleeping window of the mobile devices' wireless transceiver. The proposed mechanism - the Slow sTart Exponential and Linear Algorithm (STELA) - consists of three sleeping window adaptation phases, each phase employing a different duty cycle management function. Based on analyzing historic data regarding traffic arrival patterns, traffic modeling is used to estimate future patterns. Consequently, the specific adaptation phases are scheduled and tuned to improve energy efficiency without compromising data delivery performance. Simulation-based testing results show significant energy savings with reduced impact on network QoS achieved when STELA is used, as opposed to other existing MAC layer protocols (IEEE 802.11 and IEEE 802.16) considered in this paper for performance comparisons.

In the context of wireless user's increasing demands for better device power and battery management, this paper investigates some factors that can impact the power consumption on the energy consumption of mobile devices. The focus is on two factors when performing multimedia streaming: the impact of the traffic location within a WLAN; and the impact of the radio access network technology (WLAN, HSDPA, UMTS). The energy measurement results show that by changing the quality level of the multimedia stream the energy can be greatly conserved while the user perceived quality level is still acceptable. Moreover, by using the cellular interface much more energy is consumed (up to 61%) than by using the WLAN interface.

This demo exhibits a new application of mobile ad-hoc networks, where, a group of mobile devices are connected to allow synchronized presentation of multimedia content. The demo is in the form of an interactive tour. Participants have a mobile device and the tour is led by a guide, who takes the group on an informative tour of a locale. The tour is augmented with the presentation of multimedia content on the devices, highlighting points of interest. Content presentation is controlled by the guide and is synchronized using the ad-hoc network. This is an edutainment application but the underlying technology could be applied elsewhere including educational settings and in entertainment.

Over the past three decades, several man-made vehicles have being sent into space to explore the extra-terrestrial bodies. As the search for water and other useful substances in the extra-terrestrial surfaces increases, this exploration activity is set to dramatically increase over the next decade (2020); with NASA planning to explore the surface of Mars, Moon and other planets and satellites. In this regard, it is imperative to build an extremely energy-efficient communication system that will cover a large area in the range of hundreds of kilometers which unfortunately is absolutely not possible today. A two-hop communication mechanism that has been well researched in the literature is insufficient to cover the extremely large distance of extra-terrestrial surface. In this paper, a novel three-hop cluster-based hierarchical communication architecture is proposed which could be easily extended to higher number of hops. In particular, it allows the individual rovers to move large distances for collecting data and at the same time provide an extremely energy-efficient mechanism for continuously exploring the surfaces. The simulation results show that in a Martian surface, the proposed three-hop design results in a higher capacity as compared to a single-hop or a cluster-based two-hop design even when the rovers move 100 km.

Energy conservation has become a critical issue around the world. In smart phones, battery power capabilities are not keeping up with the advances in other technologies (e.g., processing and memory) and are rapidly becoming a concern, especially in view of the growth in usage of energy-hungry mobile multimedia streaming. The deficiency in battery power and the need for reduced energy consumption provides motivation for researchers to develop energy efficient techniques in order to manage the power consumption in next-generation wireless networks. As there is little analysis in the literature on the relationship between the wireless environment and the mobile device energy consumption, this paper investigates the impact of network-related factors (e.g., network load and signal quality level) on the power consumption of the mobile device in the context of video delivery. This paper analyzes the energy consumption of an Android device and the efficiency of the system in several scenarios while performing video delivery (over UDP or TCP) on an IEEE 802.11g network. The results show that the network load and the signal quality level have a combined significant impact on the energy consumption. This analysis can be further used when proposing energy efficient adaptive multimedia and handover mechanisms.

There is an increasing demand by an ever-growing number of mobile customers for transfer of rich media content. This requires very high bandwidth which either cannot be provided by the current cellular systems or puts pressure on the wireless networks, affecting customer service quality. This study introduces COARSE – a novel cluster-based quality-oriented adaptive radio resource allocation scheme, which dynamically and adaptively manages the radio resources in a cluster-based two-hop multi-cellular network, having a frequency reuse of one. COARSE is a cross-layer approach across physical layer, link layer and the application layer. COARSE gathers data delivery-related information from both physical and link layers and uses it to adjust bandwidth resources among the video streaming end-users. Extensive analysis and simulations show that COARSE enables a controlled trade-off between the physical layer data rate per user and the number of users communicating using a given resource. Significantly, COARSE provides 25–75% improvement in the computed user-perceived video quality compared with that obtained from an equivalent single-hop network.

In recent years, there has been an exponential increase in the number of mobile phone users. In addition, a significant growth in the demand for high-rate multimedia services over wireless networks, such as video conferencing, multimedia streaming, etc., was noted. Different solutions were proposed to support high-quality high data rate delivery to mobile users, including resource allocation techniques for packet-radio-based next generation cellular networks. In this paper, an efficient time slot allocation method-Dynamic Time Slot Partitioning (DTSP) algorithm based on statistical multiplexing is proposed for a two-hop cellular architecture. In DTSP, the available bandwidth resources are increased by partitioning each time slot into several minislots wherein different numbers of minislots are allocated to different users. The DTSP algorithm is based on asynchronous time-division multiplexing, wherein users with variable number of packets in their buffers can transmit data sequentially without any loss in the overall available resources. The key advantage of DTSP is that it can flexibly adapt to different quality of service requirements, especially when combined with adaptive modulation. It has been observed that the system capacity achieved by the DTSP algorithm in the downlink mode using adaptive modulation is up to 41 percent higher than when existing solutions are employed. In addition, DTSP results in significantly lower time for data transmission than the state-of-the-art region and time partitioning techniques.

The emerging Media Independent Handover (MIH) standard proposes to support session continuity during handover between heterogeneous networks. One of the critical features provided by MIH is an Event Service which includes predictive network degradation events, such as Link_Going_Down (LGD), which are triggered based on link layer metrics. Our results highlight the reactivness of media stream quality to network degradation. The point of degradation however, is specific to the characteristics of the class of media streaming service. Many existing event algorithms utilize static performance thresholds which are unresponsive to the requirements of individual application service classes. In this paper we propose a Service Oriented Link Triggering Algorithm (SOLTA) which triggers the LGD and Link_Down (LD) events based on link layer metrics but subject to the performance characteristics of the supported class of service. SOLTA illustrates that for 802.11, it is necessary to have aggressive service class specific, link event triggering. SOLTA also illustrates how a soft path handover approach such as Stream Control Transmission Protocols Concurrent Multi-path Transfer (SCTP-CMT) variant is necessary to support seamless session migration.

Real-time multimedia communications over vehicular ad-hoc networks (VANET) will play an extremely significant role in the next generation intelligent transport systems. In recent years, there has been an upsurge of interest in the quality-oriented adaptive multimedia delivery, including over VANET. In this paper, a hybrid IEEE 802.11p-based multihop network communication solution is presented, which makes use of both in frastructure and ad-hoc modes in order to deliver quality-oriented real-time multimedia content to high-speed vehicles. Simulation-based testing shows how multimedia delivery to vehicles when using the multihop hybrid mechanism achieves significantly higher throughput in comparison to the case when the in frastructure mode is employed and the vehicles communicate directly with the base station. This result is obtained while the average delay and packet loss in the two cases are similar.

Handover management becomes an increasingly important component of the emergent mobile Internet by maintaining mobile user's data sessions alive in the presence of user mobility. The great impact handover has on Quality of Service makes it a crucial factor in maintaining mobile user's Quality of Experience at a high level. This paper evaluates the Quality of Multimedia Streaming metric proposed by the authors in a previous paper. Quality of Multimedia Streaming is a comprehensive and flexible metric for estimating the amount of traffic each network can hold. Unlike the traditional handover algorithms which select the best network, the investigated solution estimates the capacity of each network and dynamically distributes the application traffic over the available networks accordingly. The simulation-based evaluation outlines the performance of the proposed metric and evaluates its parameters for best performance in term of user Quality of Experience.

IEEE 802.11 (Wi-Fi) is a standard feature on smart phones and an alternative to cellular networks for connectivity. Users can surf the Web, make VoIP calls and more from home Wi-Fi networks or public hotspots. At present this type of connectivity is intermittently available with limited mobility support. It is unclear if today's Wi-Fi scales for large number of users. We consider the challenges in providing Wi-Fi connectivity to mobile devices on a cellular network-like scale. The provision of quality services which rely on network connectivity, within entertainment parks is considered. Limitations of some existing communications technologies are examined and a number of important problems are quantified. Dual-radio and other solutions to the scalability problem are presented.

Over the last number of years, smart mobile devices have been rapidly replacing computers as the most commonly-used web-access devices. Mobile device capabilities have improved exponentially with time, when compared with the relatively small increase in their battery life. This has had a serious impact on these devices' practical use, especially for multimedia streaming and video-on-demand applications. This paper proposes a cross-layer Battery and Stream-Aware Adaptive Multimedia Delivery mechanism (BaSe-AMy) that monitors mobile device remaining battery level, remaining video stream duration and packet loss rate. BaSe-AMy makes use of these values to decide whether or not video quality adaptation is required in order to achieve power saving, while also maintaining good user perceived Quality of Service (QoS) levels. The proposed mechanism is evaluated and compared with a non-adaptive scheme in terms of battery duration and video quality (expressed in terms of Peak Signal-to-Noise Ratio - PSNR). It is shown that such a mechanism not only results in an increase in battery life by up to 18%, but also raises the video quality by up to 4%.

An important issue of supporting multi-user video streaming over wireless networks is how to optimize the systematic scheduling by intelligently utilizing the available network resources while, at the same time, to meet each video's Quality of Service (QoS) requirement. In this work, we study the problem of video streaming over multi-channel multi-radio multihop wireless networks, and develop fully distributed scheduling schemes with the goals of minimizing the video distortion and achieving certain fairness. We first construct a general distortion model according to the network¿s transmission mechanism, as well as the rate distortion characteristics of the video. Then, we formulate the scheduling as a convex optimization problem, and propose a distributed solution by jointly considering channel assignment, rate allocation, and routing. Specifically, each stream strikes a balance between the selfish motivation of minimizing video distortion and the global performance of minimizing network congestions. Furthermore, we extend the proposed scheduling scheme by addressing the fairness problem. Unlike prior works that target at users' bandwidth or demand fairness, we propose a media-aware distortion-fairness strategy which is aware of the characteristics of video frames and ensures max-min distortion-fairness sharing among multiple video streams. We provide extensive simulation results which demonstrate the effectiveness of our proposed schemes.

Traditionally, adaptive multimedia streaming schemes aim to adjust features such as bandwidth to respond to changing network conditions, in the hope the the user perceptual impact of any quality loss is, if not unnoticed, minimised. However, current solutions equally affect the whole viewing area of the multimedia frames, despite research showing that there are regions of the frame which are perceptually more relevant than others. This paper presents a novel eye-tracking-based adaptive scheme (ETAS) for multimedia streaming that performs transmission-related quality adjustments by selectively degrading the quality of those regions of the viewers are the least interested in, leaving perceptually relevant regions unchanged.

Handover solutions ensuring seamless connectivity and high user-perceived quality of service for a given application context are essential for multi-mode wireless devices in heterogeneous wireless network environments. A critical handover step, the network selection decision, is automatically and transparently made in the user's terminal, aiming to keep the user “always best connected”. We propose Quantified Adaptive Delay Selection (QADS), a novel multi-user-aware handover algorithm that maintains high quality of service levels for mobile users performing handover in heterogeneous wireless network environments. QADS is a user-centric solution buildt on the IEEE 802.21 Media Independent Handover standard. It addresses the problem of multiple mobile nodes performing network selection independently, using the same selection algorithm. With innovative mechanisms based on adaptive contention and randomization, the algorithm increases overall user-perceived quality of service.

The demand for multimedia services is increasing and users expect rich services at high quality levels, even while on the move and connected via different wireless networks. This paper proposes a novel signal strength-based adaptive multimedia delivery mechanism (SAMMy) that makes use of the IEEE 802.11k standard when dynamically adjusting multimedia delivery based on estimated signal strength and actual loss rates, in order to increase user perceived quality for video streaming applications in WLAN. Location and time dimensions are used together with the receive signal strength estimations in order to predict the QoS characteristics along the user's path. The proposed mechanism is evaluated by simulation and compared with a non-adaptive multimedia delivery mechanism and with two other adaptive schemes, in terms of loss, throughput and Peak Signal to Noise Ratio (PSNR). The results show that the proposed signal strength-based adaptive multimedia delivery scheme outperforms the other schemes involved making more efficient use of the wireless network resources and increasing the user perceived quality.

Inter-network mobility is achieved by allowing a mobile node to change its point of attachment to the network while preserving connectivity to its corresponding nodes. Most handover solutions proposed in the literature directly change the whole data flow from one network to another relying on only one network to transfer the entire data stream. These solutions involve a certain amount of quality degradation due to increasing loss and delay and suffer in terms of scalability, efficient resource allocation and resilience to different mobile node speeds. This paper proposes the smooth adaptive soft-handover algorithm (SASHA) which increases the quality of the multimedia delivery process when performing handover in heterogeneous wireless environment by gracefully transferring the load from one connection to the other.

Recent research on concurrent multipath transfer (CMT) and CMT with a potentially-failed destination state (CMT-PF) uses the transport layer multi-homing protocol stream control transmission protocol (SCTP) to increase application throughput by distributing transmitted data across multiple end-to-end paths. This paper investigates and evaluates the performance of CMT with partial reliability (CMT-PR) and CMT-PF with partial reliability (CMT-PF-PR), novel extensions of SCTP for real-time video distribution. The Evalvid-CMT platform was implemented in the University of Delaware's SCTP/CMT ns-2 module to perform emulation experiments in order to compare CMT and CMT-PR, CMT-PF and CMT-PF- PR, respectively. The results presented in the paper show how the CMT-PR and CMT-PF-PR outperform CMT and CMT-PF respectively. Consequently the former are suggested as strategies for real-time video concurrent multipath transmissions.

Most research on P2P multimedia streaming assumes that users access video content sequentially and passively. Unlike P2P live streaming in which the peers start playback from the current point of streaming when they join the streaming session, in P2P video-on-demand streaming VCR-like operations such as forward, backward, and random-seek have to be supported. Providing this level of interactive streaming service in a P2P environment is a significant challenge. This paper proposes a balanced binary tree-based strategy for unstructured video-on- demand distribution in P2P networks (BBTU). BBTU assumes videos can be divided into several segments which can be fetched from different peers. BBTU involves two steps: 1) balance binary tree construction based on a prefetching algorithm in order to support interactivity; 2) unstructured video dissemination over network based on gossip protocol, which is the overlay for video distribution. Analysis and simulation show how BBTU is an efficient interactive streaming solution in P2P environment.

The existing DONet-based approach uses successfully a random gossip algorithm for scalable live video streaming. This pure mesh overlay network-based solution may lead to unacceptable latency or even failure of VCR operations in video-on-demand (VoD) services where nodes usually have different playing offsets, across a wide range. This paper proposes DONet-VoD which enhances DONet in order to address issues related to VoD delivery and VCR operations. In DONet-VoD, DONet principle is employed for the video distribution over the overlay network and a novel algorithm which uses a multi-way tree structure and extra prefetching buffers at the nodes is proposed to support efficient VoD operations. Video segments are prefetched and stored in a distributed manner in the nodespsila prefetching buffer along the tree. The cooperation between DONet-based video delivery and the tree-located multimedia components enable multimedia streaming interactive commands to be performed efficiently. This paper presents and discusses the prefetching scheme, details the cooperation procedure, and then analyses the performance of the proposed DONet-VoD.

In order to accommodate the constantly growing number of vehicles on the road with which infrastructure provision is failing to cope, new means of optimizing the available road space are required. This paper presents a novel adaptive vehicle routing algorithm for TraffCon - an innovative Traffic Management System enabled by wireless vehicular networks. The algorithm combats the vehicular traffic congestion problem by seeking to optimize the usage of existing road capacity, while also minimising vehicle fuel consumption and emissions. Results demonstrate that the algorithm significantly increases road utilisation, reduces congestion, average journey times and fuel consumption in comparison with existing approaches.

Traffic congestion is a very serious problem which is becoming ever worse as the growth in the number of cars on the road significantly out-paces the provision of road capacity. This paper presents a novel vehicle routing algorithm for TraffCon - an innovative traffic management system for wireless vehicular networks - and discusses its complexity. The algorithm combats the traffic congestion problem by seeking to optimize the usage of the existing road capacity, reduce vehicle trip times and decrease fuel consumption and the consequent gas emissions. Results demonstrate that the algorithm significantly increases road capacity utilisation and consequently reduces traffic congestion in comparison with an existing approach.

As video on demand systems gain popularity, it seems likely that the desire to serve a high number of customers from limited network resources could lead to a degradation of the end- users' perceived quality. Quality-oriented adaptation scheme (QOAS) balances the need for high quality with increased network utilization when streaming multimedia. QOAS requires client-side monitoring of some transmission-related parameters, grading of the end-user's quality and feedback that informs the server about the received quality. In response to this feedback, the server adjusts the streaming process in order to maximize the end-user perceived quality in the current conditions. This paper studies the effect of delivery latency and feedback frequency on quality-oriented adaptive multimedia streaming. It also shows how high end-user perceived quality is maintained in the presence of different types of background traffic while recording a significant increase in link utilization and a very low loss rate.

This paper proposes a TCP compatible greediness control mechanism that tunes the greediness of the multimedia streaming process based on client priority, in order to make more efficient use of the wireless network and increase the overall user perceived quality. The majority of streaming solutions use rate adaptation based on congestion avoidance mechanisms that try to obtain as much bandwidth as possible from the limited network resources. However the lack of both knowledge about the characteristics of target devices, cross-layer communication and cross-protocol interaction results in fair bandwidth distribution at the transport layer, but creates unfairness at the application layer. This unfairness mostly affects user perceived quality when streaming high quality multimedia. Therefore, there is a need to allow application layer streaming applications tune the aggressiveness of transport layer congestion control mechanisms, in order to create application layer Quality of Experience fairness between competing media streams, by taking their device characteristics into account

For multimedia streaming over wireless networks, there is a trade-off between the capacity of the wireless links and the end-user perceived-quality, which can be affected by the compression scheme used, content characteristics and adaptation algorithm (if any). In this paper, this trade-off is investigated for streaming various motion content multimedia over an IEEE 802.11b-based wireless-home area network using the quality-oriented adaptation scheme (QOAS). QOAS performance is compared to that of a non-adaptive scheme when using MPEG-2 and MPEG-4 encoding in terms of average end-user perceived quality, number of streaming sessions concurrently supported, loss rate, delay, jitter and total throughput. Simulation results show that by using QOAS and MPEG-4 encoded streams a much higher number of concurrent streams are supported at an average quality above "good" level on the ITU-T five-point quality scale in comparison with other situations. In this case all the other streaming performance parameters were also significantly better.

Development in wireless access technologies and multihomed personal user devices is driving the way towards a heterogeneous wireless access network environment. Success in this arena will be reliant on the ability to offer an enhanced user experience. Users will plan to take advantage of the competition and always connect to the network which can best service their preferences for the current application. They will rely on intelligent network selection decision strategies to aid them in their choice. The contribution of this paper is to propose an intelligent utility-based strategy for network selection in this multi-access network scenario. A number of utility functions are examined which explore different user attitudes to risk for money and delay preferences related to their current application. For example we show that risk takers who are willing to pay more money get a better service.

In wireless multimedia streaming, there is a need to allow for client prioritisation in order to enable provision of end user perceived quality in direct relationship with client device importance. Currently, the same priority is given to all clients, independent of their characteristics, often resulting in unfair distribution of throughput. This paper proposes a priority-based wireless adaptive multimedia delivery scheme that enables client prioritisation during multimedia distribution over IP networks. The paper presents simulation results outlining the benefits of applying the algorithm, illustrating the improvement in bandwidth allocation and in overall end user perceived quality. The algorithm focuses on a residential wireless local area network and assigns static priorities based on device characteristics

This paper describes and formalises the service oriented heterogeneous wireless network environment (SOHWNE), the future service provision and delivery environment that supports ubiquitous user access anywhere at any time from diverse devices to a broad range of services. These services can be offered by third parties and can be accessed via one of many available networks. This paper also proposes and describes a novel algorithm for intelligent cost-oriented and performance-aware selection between available networks. This user-centric strategy focuses on the maximisation of consumer surplus when selecting the best available connection for transferring non real-time data, with user specified time constraints, in a user-centric SOHWNE

This article presents objective and subjective testing results that assess the performance of the Quality-Oriented Adaptation Scheme (QOAS) when used for high quality multimedia streaming over local broadband IP networks. Results of objective tests using a QOAS simulation model show very efficient adaptation in terms of end-user perceived quality, loss rate, and bandwidth utilization, compared to existing adaptive streaming schemes such as LDA+, and TFRCP. Subjective tests confirm these results by showing high end-user perceived quality of the QOAS under various network conditions.

Lately, multimedia-related Internet applications have become very popular, and make up an increasing percentage of network traffic. This paper presents a quality-of-transmission-orientated adaptive mechanism for streaming of pre-recorded multimedia content. It aims at maintaining the continuity of both the transmission and the remote stream play-out, at the expense of varying the stream's quality. A feedback scheme, in conjunction with a quality of transmission grading scheme, allows the server to learn the current network conditions. During transmission, the server can switch between different quality versions of the same multimedia content at certain checkpoints to modify the quality of the overall streaming process, and therefore the transferred quantity of data. Preliminary statistical and user perceptual test results from our prototype system show that by using our adaptive mechanism in increased traffic conditions, the users' satisfaction was higher than if a receiver buffering solution was used.