Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> We present a scheme for flow management with heterogeneous access technologies available indoors and in a campus network such as GPRS, 3G and Wi-Fi. Statistical learning is used as a key for optimizing a target variable namely video quality of experience (QoE). First we analyze the data using passive measurements to determine relationships between parameters and their impact on the main performance indicator, video Quality of Experience (QoE). The derived weights are used for performing prediction in every discrete time interval of our designed autonomic control loop to know approximately the QoE in the next time interval and perform a switch to another access technology if it yields a better QoE level. This user-perspective performance optimization is in line with operator and service provider goals. QoE performance models for slow vehicular and pedestrian speeds for Wi-Fi and 3G are derived and compared. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> In this paper, a first approach for evaluating the Quality of Experience (QoE) for IoT (Internet of Things) applications is presented. Firstly, a layered IoT architecture is analysed to understand which QoE influence factors have to be considered in relevant application scenarios. Secondly, we introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a vehicular MIoT application that has been used to conduct subjective quality assessments to verify the performance of the proposed approach. <s> BIB002 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> This paper addresses the issue of evaluating the Quality of Experience (QoE) for Internet of Things (IoT) applications, with particular attention to the case where multimedia content is involved. A layered IoT architecture is firstly analyzed to understand which QoE influence factors have to be considered in relevant application scenarios. We then introduce the concept of Multimedia IoT (MIoT) and define a layered QoE model aimed at evaluating and combining the contributions of each influence factor to estimate the overall QoE in MIoT applications. Finally, we present a use case related to the remote monitoring of vehicles during driving practices, which is used to validate the proposed layered model, and we discuss a second use case for smart surveillance, to emphasize the generality of the proposed framework. The effectiveness in evaluating classes of influence factors separately is demonstrated. <s> BIB003 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> The Internet of Things (IoT) opens a new horizon for network applications by virtue of wireless sensor network technologies and advancement of data analytics. Since various applications are possible in the IoT compared to conventional multimedia applications, quality of experience (QoE), which is a user's subjective satisfaction with the application, becomes more complicated for network operators to perceive. In this paper, we propose a framework of scalable QoE modeling for explosively increasing applications. In the framework, the massive amount of quality metrics in the IoT architecture are defined as physical metrics and organized into four layers; device, network, computing, and user interface. We also introduce metaphysical metrics, which are the summarized quality metrics that users of IoT applications demand. By aggregating the massive amount of physical metrics into certain metaphysical metrics and qualitatively modeling the QoE by the metaphysical metrics according to the characteristics of the applications, scalable modeling of the QoE for continuously emerging applications in the IoT becomes possible. <s> BIB004 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values. <s> BIB005 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> This work is based on the investigation of the passive Quality of Experience (QoE) monitoring of Over-The-Top (OTT) applications at the user terminal with the perspective of the Internet Service Provider (ISP). The contribution is multi-fold. Firstly, we investigate the advantages and drawbacks of QoE monitoring at the user terminal by highlighting the roles of the different entities involved in multimedia service delivery, i.e., users, OTTs and ISPs. Secondly, a survey is conducted related to user's privacy and preferences with reference to the installation of monitoring probes at the terminal. The analysis of survey results highlights the applicability of the approach in terms of users' willingness to have probes at their terminals in exchange for higher quality, discounts or extra data usage. Thirdly, we propose a multi-layered passive monitoring approach at the user terminal which considers the activation of the probes only in case quality degradation is revealed (in an event-based fashion), in contrast to conventional passive monitoring solutions whose probes send monitored data to the collection centers on regular intervals basis (constant frequency). As a case study, YouTube is considered as video streaming service in our experiments to investigate the effectiveness of the proposed approach. <s> BIB006 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> Abstract With rapid evolution of the Internet of Things (IoT) applications on multimedia, there is an urgent need to enhance the satisfaction level of Multimedia IoT (MIoT) network users. An important and unsolved problem is automatic optimization of Quality of Experience (QoE) through collecting/managing/processing various data from MIoT network. In this paper, we propose an MIoT QoE optimization mechanism leveraging data fusion technology, called QoE optimization via Data Fusion (QoEDF). QoEDF consists of two steps. Firstly, a multimodal data fusion approach is proposed to build a QoE mapping between the uncontrollable user data with the controllable network-related system data. Secondly, an automatic QoE optimization model is built taking fused results, which is different from the traditional way. QoEDF is able to adjust network-related system data automatically so as to achieve optimized user satisfaction. Simulation results show that QoEDF will lead to significant improvements in QoE level as well as be adaptable to dynamic network changes. <s> BIB007 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> As multimedia services are gaining considerable popularity in the Internet of Things, providing QoE in smart mmIoT becomes a challenging issue. This article conceptualizes a new framework named SMP, to price the QoE rather than the binary data traffic in IoT multimedia services. By introducing PaaR in QoE-driven network scheduling in mmIoTs, a new dimension of price distortion is available in multimedia resource allocation, complementing traditional rate distortion and power distortion resource allocation approaches. This article also discusses open research challenges in SMP resource allocation, including the difficulty of numerical presentation of QoE, the unavailability of packet-level distortion information, and the non-concavity of the utility functions. A case study is presented to illustrate the PaaR potentials of improving QoE through the SMP framework in mmIoTs. <s> BIB008 </s> Multimedia Internet of Things: A Comprehensive Survey <s> A. QUALITY OF EXPERIENCE (QoE) AWARE ARCHITECTURES <s> With the rapid growth of multimedia content production from massive User Equipments (UEs) in the Internet of Things (IoTs) environment, how to ensure high Quality of Experience (QoE) for UEs in the wireless IoT upstreaming services becomes a big challenge. To address the multimedia IoT QoE challenge, a game theoretical power allocation strategy is studied in the Non-Orthogonal Multiple Access (NOMA) wireless uplink IoT environment in this paper. Due to the natural non-cooperative relationships among UEs, we model the UEs’ power allocation problem in the upstreaming service as a Cournot competition game in an Oligopoly market model. In addition, factors such as the uploading content popularity, power constraints, and the energy cost of a UE are jointly considered when we build UE's QoE model. By deriving the concavity of UE's utility function, we prove the existence and uniqueness of the Nash Equilibrium power solution for each UE. Numerical simulations are carried out by considering all the system constraints in the multiple UEs scenarios. The results show that system throughput in the NOMA scheme outperforms the conventional Orthogonal Multiple Access, and the system utility performance in the proposed game solution is better than the fixed power selection strategy. <s> BIB009
According to Qualinet White Paper  , QoE is defined as ''QoE is the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations concerning the utility and enjoyment of the application or service in the light of the user's personality and current state.'' International telecommunication union (ITU) defines QoE in ITU-T Rec. P.10  as follows: ''The overall acceptability of an application or service, as perceived subjectively by the end-user.'' Subjective and objective QoE data of the M-IoT network is mostly acquired and evaluated separately. The fusion of the subjective QoE data, i.e., user's experience rating with the objective QoE data, i.e., bandwidth allocation, route selection, etc., is important to optimize the system. Huang et al. in BIB007 proposed a novel NN based selflearning self-updating framework to fuse QoE subjective and objective metrics named as QoEDF. MOS is adopted in this framework to quantify user experiences per service. Various factors affecting QoE has been presented in the article. Bandwidth allocation, jitter, and best route selection are considered as objective metrics. Two-layered NN based framework is presented to map the subjective MOS value with objective metrics. The optimization problem is formulated to optimize QoE by first selecting the best route and then assigning the required bandwidth for the selected route. Floris and Atzori in BIB003 and BIB002 proposed virtual layered based QoE-aware architecture. The term Quality of Data (QoD) is introduced to evaluate the quality or precision of acquired data by M-IoT devices, and QoS parameters are considered as influencing factors for QoD. QoE-aware framework for smart surveillance and vehicle monitoring system has been presented. The authors adopted MOS for numerically measuring QoE. Karaadi et al.  defined the term QoE and QoS in the rapport of M2H, H2H, H2H, and M2M. In the case of humans as the recipient of the multimedia services, the performance of the service is evaluated as QoE, otherwise, when a receiver is a machine, then the concept of QoT is utilized. QoT is defined as the acceptable quality to satisfy or complete a service session successfully. The bandwidth utilization is optimized by considering the minimum bandwidth at an acceptable level to meet the minimum requirement of an application. Results depict that the requirement for QoE is greater than QoT. The system can be further enhanced by utilizing ML approach to allocate minimum bandwidth as required to maintain QoT. Ikeda et al. BIB004 proposed M-IoT framework to evaluate QoE based on physical metrics and metaphysical metrics. The metaphysical metric is proposed to understand the user's quality and service requirement, which varies with applications and model them with available physical metrics to achieve desired QoE. However, this work lacks subjective experiments and qualitative modeling. SDN and Network Function Virtualization (NFV) based QoE optimization for adaptive video streaming applications is proposed in BIB005 . Video streaming comprises four main tasks, i.e., caching, encoding, forwarding, and playing back. Node selection for best-path selection is defined as the objective function subject to the constrained node in terms of resources. The proposed model is evaluated on Mininet a network emulator and OpenDaylight as an SDN controller. Performance evaluation of the model is studied based on endto-end delay, packet loss, and user's QoE. He and Wang BIB009 studied the effect of interference between Non-Orthogonal Multiple Access (NOMA) UE's on QoE at the consumer end. Authors utilize the user satisfaction method, i.e., several clicks or playtime to quantify QoE and formulated Cournot competition Oligopoly game problem to optimize power allocation among NOMA UE's to upload the acquired real-time video surveillance data to the BS with minimum interference and maintain QoE. Statistical learning, prediction, and automatic network resource management mechanism to optimize multimedia QoE has been presented in BIB001 . By statistical monitoring, the QoE is predicted, computed and managed by evaluating objective network metrics, i.e., jitter, packet loss rate, frame rate, frame resolution, data rate, codec, and data volume. MOS has been utilized for QoE. The mobile node frequently switching network has been tested for the proposed model. Ahmad et al. BIB006 presented a multi-dimensional passive QoE monitoring approach at the user terminal based on quality degradation, where the probes at UE becomes online when quality is degraded below a specific threshold. The analyzed data at UE will be sent to the service provider to take critical actions to overcome the degradation. The proposed system could be improved by the implementation of cloud-based QoE analysis and monitoring. Wang W. and Wang Q. in BIB008 introduce a novel concept of Smart Media Pricing (SMP) by implementing Price as a Resource (PaaR) instead of smart data pricing. The authors have proposed to price the enduser according to its required QoE as the criticality of a self-driving car is higher to ensure the QoE due to high data rate requirement for precision than the telepresence of a user in which bandwidth requirements are low. Premium quality content should be priced higher than an economy class user.