Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> In this work a new Universal Communications Framework (UCF) is defined, based on IMS concepts and on EPC technologies. The UCF extends the IMS concepts to a generic middleware solution that intermediates between underlying network infrastructure and various application functions by offering generalized service APIs. One use case scenario to demonstrate the overall system is the integration of non human-interacting services like mobile Machine-to-Machine (M2M) communication in the emerging Internet of Things (IoT). Furthermore, the framework is focused on non human-interacting services like mobile Machine-to-Machine communication enabled with dedicated application-driven Quality of Service (QoS). <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> Internet of Things (IoT) is now transitioning from theory to practice. This means that a lot of data will be generated and the management of this data is going to be a big challenge. To transform IoT into reality and build upon realistic and more useful services, better resource management is required at the perception layer. In this regard, Fog computing plays a very vital role. With the advent of Vehicular Ad hoc Networks (VANET) and remote healthcare and monitoring, quick response time and latency minimization are required. However, the receiving nodes have a very fluctuating behavior in resource consumption especially if they are mobile. Fog, a localized cloud placed close to the underlying IoTs, provides the means to cater such issues by analyzing the behavior of the nodes and estimating resources accordingly. Similarly, Service Level Agreement (SLA) management and meeting the Quality of Service (QoS) requirements also become issues. In this paper, we devise a methodology, referred to as MEdia FOg Resource Estimation (MeFoRE), to provide resource estimation on the basis of service give-up ratio, also called Relinquish Rate (RR), and enhance QoS on the basis of previous Quality of Experience (QoE) and Net Promoter Score (NPS) records. The algorithms are implemented using CloudSim and applied on real IoT traces on the basis of Amazon EC2 resource pricing. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> 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. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> With the rapid development of network technology, the types of streaming video services are also changing. Service providers concern about the Quality of Service (QoS) of applications, because the corresponding QoS directly affects the user's subjective perception, and user's quality of experience (QoE). This paper analyzes the streaming video service performances, including both subjective and objective QoS factors that affect QoE impact. We consider two different scenarios, one is watching sports scene, and the other is in a static viewing news scene. Three QoS parameters are considered, i.e. network delay, jitter and packet loss rate. By NS-2 simulation experiment platform, network impairments are compared in the sensitivity of different scenarios and different service performances of streaming media on the QoE of video services at different network scenarios. <s> BIB004 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> The narrowband Internet-of Things (NB-IoT) standard has been developed to provide low data rate requirement for 5G IoT systems. To prolong the battery lifetime of user equipment (UE), the extend discontinuous reception (eDRX) and power saving mode (PSM) are specified in the standard for power efficient purpose. However, it is non- trivial to configure suitable power-saving parameters to achieve power conservation while reducing transmission latency. In this paper, we proposed a QoS-guaranteed power-saving configuration prediction (QPCP) scheme to effectively reduce system power consumption with guaranteed packet delay constraints. By adopting partially observable Markov decision process (POMDP) and scheduling of base station (BS), a feasible set of power-saving parameters can be obtained. Simulation results show that the proposed QPCP scheme outperforms other existing methods for power-saving of both UE and BS with guaranteed QoS constraints. <s> BIB005 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> The proliferation of connected objects has revolutionized the traditional Internet, giving rise to the emerging Internet of Things (IoT). The IoT ecosystem is very large, and it includes smart interconnections among sensors and devices with applications in both the industrial world and customers' daily lives. As a unified standard for IoT is still under development, many challenges related to IoT must be discussed and addressed, especially those related to energy efficiency. This article tackles the challenge of energy efficiency in IoT from a novel perspective. It shows that instead of maximizing the QoS, which is generally energy costly, better energy efficiency can be achieved by targeting satisfactory QoS levels only. The approach aims to enhance energy efficiency while ensuring a desired QoS threshold. This is supported by a game theoretical solution concept referred to as the satisfaction equilibrium. Moreover, as IoT objects require self-configuring techniques to maintain the network scalability and flexibility, this article introduces fully distributed schemes in order to reach efficient satisfaction equilibria in both slow- and fast-fading channel contexts. The proposed schemes can also be adapted to achieve the maximum performance of IoT applications that desire the highest QoS levels. The performance of these algorithms is illustrated through a smart home use case scenario. <s> BIB006 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> With continuous progress of Internet of Things, multimedia analysis in it has attracted more and more attention. Specially, stereoscopic display technology plays an important role in the multimedia analysis processing. In the Internet of Things system, the quality of stereoscopic image will be reduced in the transmission process. In this mode, it will have a great impact on multimedia analysis to judge whether the quality of stereoscopic image meets the requirements. In this paper, a new no-reference stereoscopic image quality assessment model for multimedia analysis towards Internet of Things is built, which is based on a deep learning model to learn from the class labels and image representations. In our framework, images are represented by natural scene statistics features that are extracted from discrete cosine transform domain, and a regression model is employed to shine upon the quality from the feature vector. The training process of the proposed model contains an unsupervised pretraining phase and a supervised fine-tuning phase, enabling it to generalize over the whole distortion types and severity. The proposed model greatly shows the correlation with subjective assessment as demonstrated by experiments on the LIVE 3-D Image Quality Database and IVC 3-D Image Quality Database. <s> BIB007 </s> Multimedia Internet of Things: A Comprehensive Survey <s> B. QUALITY OF SERVICE (QoS) AWARE ARCHITECTURE <s> Traditionally, hybrid optical-wireless networks (Fiber-Wireless - FiWi domain) and last-mile Internet of Things edge networks (Edge IoT domain) have been considered independently, with no synergic management solutions. On the one hand, FiWi has primarily focused on high-bandwidth and low-latency access to cellular-equipped nodes. On the other hand, Edge IoT has mainly aimed at effective dispatching of sensor/actuator data among (possibly opportunistic) nodes, by using direct peer-to-peer and base station (BS)-assisted Internet communications. The paper originally proposes a model and an architecture that loosely federate FiWi and Edge IoT domains based on the interaction of FiWi and Edge IoT software defined networking controllers: the primary idea is that our federated controllers can seldom exchange monitoring data and control hints the one with the other, thus mutually enhancing their capability of end-to-end quality-aware packet management. To show the applicability and the effectiveness of the approach, our original proposal is applied to the notable example of multimedia stream provisioning from surveillance cameras deployed in the Edge IoT domain to both an infrastructure-side server and spontaneously interconnected mobile smartphones; our solution is able to tune the BS behavior of the FiWi domain and to reroute/prioritize traffic in the Edge IoT domain, with the final goal to reduce latency. In addition, the reported application case shows the capability of our solution of joint and coordinated exploitation of resources in FiWi and Edge IoT domains, with performance results that highlight its benefits in terms of efficiency and responsiveness. <s> BIB008
The quality of telecommunication services is defined by ITU in recommendation ITU-T E.800  as: ''Totality of characteristics of a telecommunications service that bear on its ability to satisfy stated and implied needs of the user of the service.'' Microsoft defines network QoS in [161] technical white paper as: ''Network QoS refers to the ability of the network to handle this traffic such that it meets the service needs of certain applications.'' A framework to assess the quality of stereoscopic images for 3D product modeling in M-IoT has been presented in BIB007 . The authors proposed a blind image Deep Belief Network (DBN) based on natural science feature extraction and Support Vector Regression (SVR) for training. More effective machine and Deep Learning (DL) algorithms could be adopted to reduce the image distortions. Bellavista et al. in BIB008 proposed an SDN controller based Fibre optics-Wireless (FiWi) and edge M-IoT hybrid network architecture to optimize QoS in HetNet. Resource allocation, i.e., bandwidth, is considered as QoS optimization metrics. FiWi and Edge M-IoT hybrid network enhance the performance by communicating via the SDN controller. Reserved and excess bandwidth are offered by FiWi network to edge network in case of spontaneous network addition at consumer end via SDN heads. SDN based HetNets integration offers better network management. Statistical QoS metrics analysis of HetNets gateway is studied in BIB003 for betterment in M-IoT network designing. PLR, throughput, and delay are considered as QoS data. Results show that delay is the same for all types of devices. However, PLR and throughput affect more on low data rate gateways as compared to high data rate gateways. Aazam et al. BIB002 proposed a fog resource estimation and utilization to improve QoS based on service Relinquish Rate (RR) and previous Net Promoter Score (NPS) . Resources are managed according to past Service Level Agreement (SLA) or disagreements. Fog computation enhances the performance of the system model by equipping the devices to estimate and utilize resources more efficiently. Quality-aware Universal Communication Framework (UCF) to handle mobile connectivity and reduce signaling overheads repeatedly between end devices for M2M communication has been presented in BIB001 . Network QoS parameters have been analyzed in BIB004 to evaluate to satisfy QoE for IoVT at various network conditions. QoS-aware framework for the Narrow-Band Internet of Things (NB-IoT) network by utilizing a Partial Observable Markov Decision Process (POMDP) to optimize energy consumption is outlined in BIB005 . To conserve energy, Poison distribution in POMDP is used to estimate traffic arrival rates and adopt dynamic network configuration changes according to future traffic prediction. The proposed model conserves power for BS and UE. Implementation of various optimization problems and ML algorithms to predict traffic arrival can further improve the system. Web Real-Time Communication (WebRTC) for video conferencing in connection with cloud computation has been studied in  . The proposed model analyzes QoS parameters in comparison with WebRTC in connection with local networks. Traffic load and the computational delay has been considered as QoS metrics for analysis. Elhammouti et al. BIB006 form a game theory problem named satisfaction equilibrium with an objective function to optimize energy instead of maximizing QoS. The authors have highlighted key motivation for optimizing energy subject to achieve satisfactory QoS constraint. Several applications related to multimedia have specific data rate requirements, therefore maximizing the QoS above that level will waste the energy. The energy management can be further enhanced by allocating power based on the intrinsic requirement of the application.