This paper considers the cell-free integrated sensing and communication (CF-ISAC) networks utilizing reconfigurable intelligent surface (RIS)-mounted uncrewed aerial vehicles (UAVs). We aim to maximize the sum of weighted sum rate within the whole ISAC period by jointly optimizing access points (APs)’ transmit beamformings, RISs’ phase shifts, user-RIS association, and UAVs’ locations. To deal with a highly complex non-convex optimization problem, we propose an alternating optimization solutions by decomposing the original problem into three subproblems. In particular, for optimizing APs’ transmit beamformings, RISs’ phase shifts, and user-RIS association, we convert the log-sum problem into a quadratically constrained quadratic programming problem using the Lagrangian dual principle and multi-ratio fractional programming. For optimizing UAVs’ locations, the successive convex approximation technique is used to transform it into a convex problem. Simulation results highlight the considerable performance advantage of the proposed network compared to benchmark schemes employing fixed RISs, without RIS-mounted UAVs (URISs), and collocated network with URISs.

In this article, we consider a new unmanned ariel vehicles (UAV)-aided intelligent wireless sensing scheme, where the UAVs are deployed for smart sensing and collecting data from Internet-of-Things (IoT) devices. In particular, we propose optimal UAVs’ path planing approaches for minimizing the completion time and total energy consumption of the UAVs’ deployment for data collection. Two optimal schemes, namely, optimal energy consumption by peer-to-peer UAV-IoT sensing networks and optimal energy consumption by clustering UAV-IoT sensing networks, are considered. The low-complexity procedures of our advanced optimization techniques are suitably applied to disaster relief networks when the solving time must be strictly adhered to. Our real-time optimization algorithms result in low computational complexity with fast deployment and low processing time for solving the problem of tracking and gathering sensor data, i.e., in very short time (milliseconds). Through simulations results we demonstrate that our proposed approaches in UAV-aided intelligent IoT wireless sensing are suitable for time-critical mission applications such as emergency communications, public safety, and disaster relief networks.

Video streaming applications and services (VASs) consume an enormous amount of scarce resources in mobile devices and cellular wireless networks due to the demand for high data rates of video streaming. The limited resource of wireless media and unreliable nature of wireless channels in cellular networks make VASs challenging to deliver videos at high quality of experience (QoE). Therefore, in this paper, we propose a femtocaching framework of QoE-oriented resource efficiency optimization for high performance of cooperative VASs over 5G two-tier cellular networks, where the collaboration between macro base stations (BSs) and femtocells are exploited to efficiently deliver videos to mobile users (MUs). Our proposed framework aims at solving two problems. The first problem is how to cache the videos in femtocells to minimize the bandwidth resource consumed at the BSs and wasted at femtocells while guaranteeing high hit rate and utilizing the available storage resource of femtocells. The second one is how to encode the videos into descriptions and assign them to each femtocell for transmission, so as to minimize the reconstructed distortion of received videos for high playback quality at the MUs. The simulation results are further provided to demonstrate the benefits of the proposed framework.

This study considers a dual‐hop cognitive inter‐vehicular relay‐assisted communication system where all communication links are non‐line of sight ones and their fading is modelled by the double Rayleigh fading distribution. Road‐side relays (or access points) implementing the decode‐and‐forward relaying protocol are employed and one of them is selected according to a predetermined policy to enable communication between vehicles. The performance of the considered cognitive cooperative system is investigated for K th best partial and full relay selection (RS) as well as for two distinct fading scenarios. In the first scenario, all channels are double Rayleigh distributed. In the second scenario, only the secondary source to relay and relay to destination channels are considered to be subject to double Rayleigh fading whereas, channels between the secondary transmitters and the primary user are modelled by the Rayleigh distribution. Exact and approximate expressions for the outage probability performance for all considered RS policies and fading scenarios are presented. In addition to the analytical results, complementary computer simulated performance evaluation results have been obtained by means of Monte Carlo simulations. The perfect match between these two sets of results has verified the accuracy of the proposed mathematical analysis.

In wireless multimedia sensor systems (WMSSs), the devices are equipped with multiple energy‐constrained camera sensors (CSs) distributed over bandwidth‐constrained and lossy wireless channels, in catastrophe‐prone areas. Meanwhile, multimedia applications, e.g. video streaming, require considerable energy and bandwidth resources to gain long lifetime and high streaming quality. This study proposes an energy, bandwidth, and quality (EBQ) optimisation framework for green two‐tiered WMSSs. The first tier contains the CSs and the second tier includes cluster heads (CHs) selected from the CSs with higher available energy and processing capacity. In the EBQ optimisation framework, a rate allocation optimisation problem is formulated under given constraints of available backhaul bandwidth of the CHs and quality of received videos at base stations (BSs). This problem is solved for optimal encoding rates to packetise each video captured from different environments into multiple descriptions for transmission. Consequently, the average energy consumption per CS is minimised for long lifetime while conserving the bandwidth of the CHs and guaranteeing high quality of received videos for the purpose of monitoring at the BSs. Simulations demonstrate that the proposed EBQ optimisation framework can efficiently enhance the performance of green two‐tiered WMSSs in terms of minimum energy consumption, bandwidth efficiency, and high quality.

During a disaster, communication systems are partially (or completely) interrupted with very limited resources due to infrastructures destruction and hence the lack of essential services. Meanwhile, the demand for communication reaches its highest peak ever since users need to contact loved ones and make sure they are safe, inform first responders and local governments about surrounding conditions, and receive urgent instructions for safety and resilience. Rather than only high quality of service (QoS), the affected users now require high quality of sustainability (QoSus) of communications that are characterized by high response, interoperable and robust connections, high hit rate and delivery capacity of contents, and resource savings. In this paper, we therefore design a QoSuS model for Mobile Ad Hoc Networks (MANET) to reduce as much harm as possible when a disaster strikes. To this end, we propose optimization problems that we solve for high QoSus of MANET in three-tier cellular networks (i.e., macrocells, femtocells, and mobile devices). Our optimal results include a number of replicas of each cached content, a set of femtocells to cache the replicas, a set of cellular users to share their sub-channels with other mobile devices, and a set of relay nodes for mobile device-to-device communications. We further take into account the constraints of co-channel interference due to many simultaneous channel accesses, storage resource of femtocells, and energy resource of mobile devices, to efficiently gain high performance of solution. Simulation results demonstrate that our proposed solutions provide the best possible QoSus under such severe conditions.

The proliferation problem of video streaming applications and mobile devices has prompted wireless network operators to put more efforts into improving quality of experience (QoE) while saving resources that are needed for high transmission rate and large size of video streaming. To deal with this problem, we propose an energy-aware rate and description allocation optimization method for video streaming in cellular network assisted device-to-device (D2D) communications. In particular, we allocate the optimal bit rate to each layer of video segments and packetize the segments into multiple descriptions with embedded forward error correction (FEC) for realtime streaming without retransmission. Simultaneously, the optimal number of descriptions is allocated to each D2D helper for transmission. The two allocation processes are done according to the access rate of segments, channel state information (CSI) of D2D requester, and remaining energy of helpers, to gain the highest optimization performance. Simulation results demonstrate that our proposed method (named OPT) significantly enhances the performance of video streaming in terms of high QoE and energy saving.

Due to the limited resources and dynamically varying nature of wireless links, guaranteeing high quality demands for a large number of heterogeneous users is very challenging in video streaming over wireless networks. In this paper, we introduce a layered multiple description coding with an embedded forward error correction scheme (LMDC-FEC). The combination of layered MDC and FEC aims at coping with not only the diverse bandwidth and unreliability of wireless links but also the heterogeneous user devices. We further propose a joint replication density and rate allocation (RD-RA) optimization problem in the context of video on-demand systems (VoDs) over wireless mesh networks (WMNs), and employ a genetic algorithms based approach to solve the optimization problem. Our objective is to elaborately distribute proper replication density for each video segment and allocate optimal bit rate to each layer of each segment in order to gain high user-perceived quality (UPQ) with small consumption of storage resource. By this method, both optimal replication densities and rate allocations are found in accordance with the access rate of segments, the diverse loss characteristics of descriptions in each segment, and the rate-distortion relationship of segments, so as to thoroughly optimize UPQ and storage resource consumption. Simulation results demonstrate that our proposed method significantly enhances the streaming performance of VoDs over WMNs.

In this paper, we aim at maximizing quality playback of received video streaming perceived by users in video on-demand systems (VoDs) over wireless mesh networks (WMNs). To do so, we exploit the benefits of both multi-source multi-path (MSMP) delivery scheme and broadcast nature of wireless media in WMNs. In this way, a new user can efficiently receive the descriptions of a video streaming from different peers (i.e., the available users who have cached the video) via the best parallel selected paths between each peer-user pair. Based on MSMP delivery scheme, we propose an optimization problem and solve it for optimal encoding rate (i.e., optimal bit allocation) of the video streaming encoded by multiple description coding (MDC) in order to provide the user with maximum reconstructed quality playback. In this technique, the optimal bit allocation is found in connection with not only the skewed lossy characteristic of multi-path but also the skewed intra popularity of the video streaming modeled by Zipf-like distribution. Furthermore, we apply MDC embedded forward error correction (MDC-FEC) for more robust in video transmission. Simulation results demonstrate that the proposed method efficiently improves the quality playback of MSMP P2P video streaming in VoDs over WMNs.

In this paper, we propose a cross-layer optimization approach for replication of video streaming over multihop wireless network by jointly taking into account application, network, MAC, and physical layers. Specifically, we design a middleware replication strategy layer with a stack profile, in which parameters across the hops and all considered layers are exchanged for optimal video replication. The simulation results demonstrate that our method improves the replication strategy performance in terms of user-perceived video quality. In turn, it satisfies the heterogeneous receive bandwidth constraint of users for efficiently using the network bandwidth compared to other optimal replication strategies without cross-layer approach.

In this paper, we consider the replication strategy for the applications of video streaming in wireless mesh networks (WMNs). In particular, we propose a closed-form of optimal replication densities for a set of frames of a video streaming based on exploiting not only the skewed access probability of each frame but also the skewed loss probability and skewed encoding rate-distortion information. The simulation results demonstrate that our method improves the replication performance in terms of user-perceived quality (UPQ) including: 1) minimum average maximum reconstructed distortion for high peak signal-to-noise ratio (PSNR), 2) small reconstructed distortion fluctuation among frames for smooth playback, and 3) reasonable average maximum transmission distance for continuous playback. Furthermore, the proposed strategy consumes small storage capacity requirement for saving the resource of network compared to other existing optimal replication strategies.

In this paper, we analyze the maximum likelihood decoding performance of non-regenerative cooperation employing Alamouti scheme. Specifically, we derive two closed-form expressions for average symbol error probability (SEP) when the relays are located near by the source or destination. The analytical results are obtained as a single integral with finite limits and an integrand composed solely of trigonometric functions. Assessing the asymptotic (high signal-to-noise ratio) behavior of SEP formulas, we show that the distributed-Alamouti codes achieves a full diversity order. We also perform Monte-Carlo simulations to validate the analysis.