Multimedia Internet of Things: A Comprehensive Survey <s> G. QoE RELATED RESEARCH DIRECTION <s> Video-on-demand streaming services have gained popularity over the past few years. An increase in the speed of the access networks has also led to a larger number of users watching videos online. Online video streaming traffic is estimated to further increase from the current value of 57% to 69% by 2017, Cisco, 2014. In order to retain the existing users and attract new users, service providers attempt to satisfy the user's expectations and provide a satisfactory viewing experience. The first step toward providing a satisfactory service is to be able to quantify the users' perception of the current service level. Quality of experience (QoE) is a quality metric that provides a holistic measure of the users' perception of the quality. In this survey, we first present a tutorial overview of the popular video streaming techniques deployed for stored videos, followed by identifying various metrics that could be used to quantify the QoE for video streaming services; finally, we present a comprehensive survey of the literature on various tools and measurement methodologies that have been proposed to measure or predict the QoE of online video streaming services. <s> BIB001 </s> Multimedia Internet of Things: A Comprehensive Survey <s> G. QoE RELATED RESEARCH DIRECTION <s> We tackle the challenging problem of streaming multiuser videos over the downlink of a cellular cognitive radio network (CRN), where each cognitive user (CU) can sense and access multiple channels at a time. Spectrum sensing, channel assignment, and power allocation strategies are jointly optimized to maximize the quality of service (QoS) for the CUs. We show that the formulated mixed integer nonlinear programming (MINLP) problem can be decomposed into two subproblems: 1) SP1 for the optimal spectrum sensing strategy and 2) SP2 for the optimal channel assignment and power allocation, without sacrificing optimality. We show that SP1 can be optimally solved if there is no restriction on the sensing capability for each CU, and develop a column generation (CG)-based algorithm to solve SP2 iteratively in a distributed manner. We also develop a heuristic algorithm for spectrum sensing with greatly reduced requirement on CU hardware, while still achieving a highly competitive sensing performance. We analyze the proposed algorithms with respect to complexity and time efficiency, and derive a performance upper bound. The proposed algorithms are validated with simulations. <s> BIB002
User satisfaction of multimedia content in M-IoT is measured by exploiting QoE subjective metrics. Recent studies of QoE evaluation for M-IoT applications are conducted with primary QoE metrics that are packet loss, MOS, DMOS, and user satisfaction. Other QoE metrics such as buffering time, data rate, interruption time, and failure rate should also be used for evaluation of M-IoT (as discussed in BIB001 ). Most of the work on M-IoT considers network performance metrics (QoS metrics) that are jitter, end-to-end delay, bit error rate, and frame loss. Both QoS and QoE metrics should be jointly evaluated to achieve better multimedia quality and network services BIB002 .