Unmanned aerial vehicles (UAVs) can extend the coverage of wireless networks due to their high mobility and the favorable radio propagation characteristics. This paper studies the trajectory optimization of UAV that are acting as radio relays. The optimization is based on predicted user locations (UTO-PUL) to assist communication to the ground users who are unable to get coverage from the base station (BS). The existing work on trajectory design has considered several optimization approaches as well as reinforcement learning (RL) algorithms. All the algorithm takes into consideration the existing state of the network such as the channel conditions, initial positions and computes the destination of the UAV based on it. The proposed algorithm is designed to also consider the predicted user mobility of the future instances. The objective is to ensure the ground users are connected to the BS. The proposed UTO-PUL algorithm's performance is evaluated using simulations in a scenario with challenging terrain, where the proposed algorithm reduced the probability of users having no coverage by between 45% to 85% compared to non-predictive approaches, and achieved gains in median downlink signal power of 14 dB compared with a deep reinforcement learning (DRL) algorithm.

Motivated by the proliferation of smart applications and the ever accumulating concerns of data privacy, a distributed deep learning (DL) model named federated learning (FL) has been emerging. FL enables the model learning in a distributed manner without sending data from all users to a centralized hub. In this paper, we consider FL for resource allocation in roadside units (RSUs)-assisted moving network. In our proposed work, we investigate resource allocation in moving network with RSUs integrated along the roads that serve moving small cells (moSCs) deployed on trams travelling with deterministic mobility. The proposed algorithm trains the resource allocation model in a distributed manner, in which each RSU exploits its computational power and the training data of its associated moSCs to generate a shared model. We provide numerical results to validate our proposed algorithm.

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

Quality of service (QoS), ubiquitous connectivity and efficient utilization of spectrum are the main concerns for vehicular users inside public transport (buses, trains etc.) in moving environments. Deployment of spectrum efficient Non-orthogonal multiple access (NOMA) enabled moving small cells can be a promising solution. However, resource allocation and pairing of backhauls for moving small cells is a challenging task due to load of small cells and Doppler effects created by time varying channel. Thus, to incorporate the mobility effect and increasing demand of spectrum, in this work we investigate a hybrid architecture for load based joint backhaul pairing and resource allocation of NOMA enabled moving small cells that maximizes spectral efficiency (SE) during on-peak instants and minimizes system complexity during off-peak instants. Since, the optimization problem formulated is NP-hard, therefore we solve it with the help of a heuristic, storage based moving small cell pairing algorithm (SMSPA). Our performance evaluations show that our proposed scheme, SMSPA, outperforms the benchmark pairing schemes in terms of data rate, bandwidth occupancy, SE during on-peak instants and reduce time complexity during off-peak instants.

Mobile edge computing (MEC) has emerged as a new paradigm to assist low latency services by enabling computation offloading at the network edge. Nevertheless, human mobility can significantly impact the offloading decision and performance in MEC networks. In this context, we propose device-to-device (D2D) cooperation based MEC to expedite the task execution of mobile user by leveraging proximity-aware task offloading. However, user mobility in such distributed architecture results in dynamic offloading decision that instigates mobility-aware task scheduling in our proposed framework. We jointly formulate task assignment and power allocation to minimize the total task execution latency by taking account of user mobility, distributed resources, tasks properties, and energy constraint of the user device. We first propose Genetic Algorithm (GA)-based evolutionary scheme to solve our formulated mixed-integer non-linear programming (MINLP) problem. Then we propose a heuristic named mobility-aware task scheduling (MATS) to obtain effective task assignment with low complexity. The extensive evaluation under realistic human mobility trajectories provides useful insights into the performance of our schemes and demonstrates that, both GA and MATS achieve better latency than other baseline schemes while satisfying the energy constraint of mobile device.

Unmanned aerial vehicles (UAVs) are expected to be extensively used as an integral part in the future generations of communication networks, to provide ubiquitous connectivity. The mobile nature of UAVs make them a tempting candidate to provide seamless connectivity in environments where the installation of conventional terrestrial base stations (BS) is not feasible. Nonetheless, there are major deployment issues related to optimal placement of UAV-mounted base stations (UAV-BSs) due to limited number of UAV-BSs, limited energy availability and trade-off between coverage area and its altitude. In this paper, we address UAV-BSs placement issues by proposing a novel Machine learning (ML) based intelligent deployment mechanism. More specifically, for intelligent deployment of UAV-BSs based on energy, computational power, nature of available data and criticality of the scenario, we use two different approaches: Support Vector Machine (SVM) and Deep Learning (DL), which is composed of sequential time series learning process. Moreover, to address the security and privacy challenges emanating from the wireless connectivity and untrusted broadcast nature of UAV-BSs, we propose a Blockchain-based novel information-sharing scheme. To evaluate the performance of our combined secure and intelligent proposed approach, we have improved energy consumption by almost twice in contrast with the normal deployment of UAV-BSs.

This paper considers device-to-device (D2D) network with Simultaneous Wireless Information and Power Transfer (SWIPT) enabled devices to ensure self-sustained communication in situations like disasters. Such direct link networks can ensure connectivity with devices having drained back-up, when trapped in collapsed infrastructure, through mutual sharing of energy on RF link. To guarantee successful execution of SWIPT session for an isolated device in wake of disasters, it is pertinent to select a reliable peer with ultimate aim to maximize link Energy Efficiency (EE). In practice, Energy Harvesting (EH) is not achievable after Information Decoding (ID); however, it has been made possible through splitting the signal in the time domain. Selection of D2D peer for self-sustained communication with an objective to maximize EE through optimum time based splitting of signal has not been extensively studied. In this paper to manifest the aforesaid goal, we worked out a joint problem of peer association and time switching ratio allocation with an objective to maximize the EE for a device contained under collapsed infrastructure. We propose an Energy efficient Peer Selection and Time switching Ratio Allocation (EPS-TRA) algorithm to solve the proposed mixed integer problem. Numerical results validate our proposed approach in acquiring better EE when compared with Uniform Allocation Scheme of time slots for EH & ID. Furthermore, results explain how EE of the link varies with the choice of constrained variables i.e., data rate and harvested energy.

With the emergence of IoT era and the increased pressure on networks placement of small cells in cellular architecture plays a significant role in boosting the system throughput while truncating the power and energy consumption of the network. To meet the growing demand of data traffic, wireless content caching can be cost effective solution in terms of backhaul energy and capacity while providing effective solution to meet the user demand and reduce the traffic in small cell network. Energy efficiency is a major concern for sustainable development of wireless networking-based IoT. Factors affecting the energy efficiency (EE) of the network are backhaul capacity, power and energy limitation, at the macro base stations. In this paper, we have investigated a file placement strategy in a cooperate small cell network with an objective of maximizing EE. We propose an energy efficient cooperative caching scheme (EECCS) in which files are placed in cooperative small cell base station (SBS) in such a manner that small cell base station can access files from the caches of other small cell base station to maximize the energy efficiency. Numerical results show that energy efficient cooperative caching scheme provides results that validate and characterize the implementation of the proposed algorithm (EECCA).

Millimeter wave (mm-wave) communication technology promises to provide higher data rates as the spectrum is highly under-utilized and more amount of spectrum can be allocated. However, mm-wave cannot travel longer distances and it is sensitive to blockages. The former issue can be dealt with via the small cell technology. Small cell technology can increase the spectral efficiency of the system if the resources are efficiently utilized. The transmission distance between the mobile user and the small cell access point is reduced and it makes an ideal candidate for the use of mm-wave. However, the latter issue of blockages still needs to be handled in order to exploit the full benefits of mm-wave in small cell network. In this paper, a blockage-aware allocation of resources for a mm-wave small cell network is investigated. Our objective is to maximize the downlink sum-rate of all users such that the quality of service (QoS) and power constraints are satisfied. The formulated problem takes into consideration the presence of blockages and the best link (both LOS and relay, only LOS, only relay) possible. We propose a blockage-aware relay selection and power allocation algorithm (BARSPAA) for mm-wave in small cells. The BARSPAA algorithm is compared with exhaustive and bounded exhaustive search algorithms. Numerical results show that the proposed BARSPAA achieves a significantly close performance while the algorithm complexity is much reduced.

Wireless backhaul of outdoor small cells is a cost-effective solution in a dense heterogeneous network as it reduces the need to provide a wired connection for each small cell access point to the core network. On the other hand, non-orthogonal multiple access (NOMA) has emerged as a promising technology to improve the spectral efficiency of a network. This paper investigates the impact of applying NOMA at the backhaul of small cells to enhance the spectral efficiency of the system. However, this requires a careful pairing of desired small cells to increase the system performance of NOMA. In this regard, a joint pairing and resource (bandwidth and power) allocation scheme for the backhaul of small cells is studied based on the load of the small cells. Furthermore, our performance evaluation shows that the proposed scheme outperforms the existing user pairing approaches in terms of achieving high spectral efficiency.

Mobile cellular users traveling in city buses are experiencing poor quality of signals due to the interference and the large number of mobile devices. To enhance the Quality-of-Service (QoS), deployment of small cell networks in city buses is a promising solution. The deployment of small cells in vehicular environment makes the resource allocation more challenging because of the dynamic interference relationships experienced by them. Therefore, resource allocation in vehicular environment within moving small cells (MSCs) needs to be handled carefully. In this study, we investigate the problem of resource allocation in city bus transit system with multiple routes. Then, we propose a Percentage Threshold Interference Graph (PTIG) based allocation of resources to MSCs in a network. City buses of multiple routes travel with variable speed and may share some of the same road segments which make it difficult to extract the exact interference patterns between them. Therefore, Long Short Term Memory (LSTM) neural networks are used to predict the city buses locations. The predicted locations of city buses are then used to generate PTIG by finding the dynamic interference relationship between MSCs. Graph coloring algorithm is used to allocate the resources to PTIG. Numerical results are presented to show the comparison of resource allocation using PTIG and Time Interval based Interference Graph (TIIG) in terms of resource block utilization and time complexity.

Smart grid is a next generation electricity network that transmits electricity to end users and enable two way digital communication for providing remote reading and other advanced metering functions. For Smart grid domains to interact with each other, a reliable communication is required. This reliable two-way communication can be achieved with the use of small cells technology (femto, pico, etc), relay, or any of the transmission scheme proposed in 5G systems. With the use of femto cells and relay nodes in the network, the allocation of resources for the purpose of smart grid communication needs to be done efficiently. Power being one of the resource has been studied for its allocation in a femto cell network for Smart grid communication, however, transmission reliability and interference is generally ignored. In this paper, we investigate the problem of power allocation in a Neighborhood Area Network (NAN) with femto cell and relay technology as a communication mechanism. We formulate the problem as a power minimization problem such that the transmission reliability is ensured. Our performance evaluation shows that the average transmit power required for transmission through femto cell is 99% less as compared with cooperative transmission.

In this paper, we jointly consider partial offloading and resource allocation to minimize the sum latency with energy efficiency for multi-user mobile-edge computation offloading (MECO) system based on orthogonal frequency-division multiple access (OFDMA). We formulate mixed integer non- linear programming (MINLP) sum latency minimization problem considering the edge execution delay, desired energy consumption for local computation, OFDMA, QoS, transmission power in uplink and edge computation capacity constraints. We propose that a user can make use of multi-channel transmissions to reduce the transmission delay for task with large data size. We first derive an expression to determine optimal offloading fraction such that edge computing delay is less than the local execution delay and energy consumed for local execution does not exceed the desired limit. Then, we transform the original problem into communication and computation resource allocation problem and propose a suboptimal low complexity algorithm to find the resource allocation. The simulation results show that the proposed scheme achieves 17% and 25% better performance than random and complete offloading schemes, respectively.

Due to scarce energy resources and large power consumptions, Wireless Power Transfer (WPT) technology is gaining increasing interest in 5G networks and the Internet of Things (IoT). However, when data communication is conducted concurrently with WPT, large interference signals may be induced which degrade performance significantly. In LoRaWAN networks, a popular IoT technology, the problem is further exacerbated by the potential of the interference signals to jam the join procedure by flattening the Received Signal Strength (RSS), thus compromising system security. In this work, we develop a mathematical framework which allows the characterization of the join procedure and communication in LoRaWAN networks as a result of the interference effects induced by WPT devices. The conducted analysis and the relevant numerical results are used to characterize the interference effects and join procedure failure ratio with respect to the power, distance and number of WPT devices. The flattening effects of RSS are shown via simulations and its implications on the network security are also demonstrated. The presented work aims at providing design guidelines for the system, so that communication performance degradation as well as network security deterioration as a result of the WPT effects is kept within acceptable levels.

Non-Orthogonal Multiple Access (NOMA) scheme, well known for its performance benefits in terms of capacity, is being considered in fifth Generation (5G). The use of NOMA to a Multiple Input Multiple Output (MIMO) system provides significant gain in data rate. This gain can be further increased by using cooperative communication. A Multi User (MU)-MIMO system is known for its overall high performance in terms of capacity, which, if combined with NOMA and a relay has the potential to further enhance the system performance. In this paper, we have investigated the capacity of a MU-MIMO NOMA system, using a multi-antenna Two-Way Relay (TWR). We have used a half duplex Decode and Forward (DaF) relay, shared by two group of users. We derived the ergodic capacity from the outage probability. Due to the system's complexity an exact analysis is difficult to compute therefore, we have derived a lower bound of the outage probability. The upper bound of the sum ergodic capacity from the lower bound of outage probability of the system is determined and closed form expressions for the upper bound of the capacity are derived. The results are verified by Monte Carlo simulations.

With the advent of D2D communication and social computing i.e Online Social Networks (OSNs); many challenges have surfaced which are yet to be addressed. Among these, one of the most important is the selection of peer, may it be for relay selection, Simultaneous Wireless Information and Power Transfer (SWIPT) or opportunistic communication during disaster. Conventionally peer selection is performed based on high offered data rate or low latency whichever is desirable; however, reliability of peer for successful transmission is ignored which results in premature termination of session. Using OSNs and social relations among people at physical layer, in this paper we have modeled intended communication session at D2D network layer in a single cell scenario where a reliable device is selected from proximity among competing nodes. Joint distribution functions have been derived considering parameters defining human social relation in physical and OSN's domain. Weighted dependency b/w random variables i.e number of interactions (E) between nodes, mean contact duration (ΔT) and mean interval b/w interactions (ΔI) have been modeled as probability distributions to study the reliability of User Equipment (UEs) above certain variable threshold. Performance of theoretical model has been verified using simulation scenario where time based distribution of nodes has been carried out using Independent Poisson Point Process (IPPP). Simulation results show how reliability of a node is affected with its mobility and social interaction.

Device-to-device (D2D)-enabled Internet-of-things promises higher spectral efficiency and system capacity by sharing the cellular spectrum and offloading the cellular traffic. However, it poses new challenges in terms of resource allocation because of interference from D2D to base station and vice versa in the downlink. Moreover, energy efficient operation of the network becomes a major challenge because of unattended operation of devices. In this study, we investigate resource allocation for the energy-harvesting-aided D2D communication underlaying cellular network. We formulate a joint subcarrier assignment and power allocation problem for multiple cellular and energy harvesting direct D2D links to maximize the overall sum rate subject to quality of service, subcarrier reuse, power, and energy harvesting constraints. The problem is formulated as a mixed integer nonlinear programming and is difficult to be solved in polynomial time. Therefore, a low complexity algorithm named energy harvesting and gain-based resource allocation (EHGRA) is proposed, which determines reuse partners considering interference among D2D and cellular links, and then, allocates power optimally to them such that the sum rate is maximized. Our performance evaluation demonstrates that energy harvesting can boost up the system performance and at the same time can achieve higher sum rate over short frame duration. Comparison shows that EHGRA performs better than the existing algorithms in terms of overall sum rate.

Small cells provisioned with device-to-device (D2D) communication has the potential to meet the high capacity requirements for the indoor environments such as shopping malls etc. Enabling D2D communication under-laying cellular networks has paved the way for increased spectrum efficiency. It is critical to consider mutual interference between D2D and cellular users to exploit the full benefits of smaller distances of D2D and resource reuse. The idea of resource reuse has been studied for D2D in macro cells. However, reuse in neighbouring small cells involving D2D communication has not been explored. In this paper, we investigate resource sharing in a multi small cell network where small cell D2D users and neighbour small cell uplink users share resources. We formulate this as an optimization problem with an objective of minimizing the number of resource blocks used such that the interference and quality of service constraint is satisfied. We propose a spatial separation based reuse (SSBR) algorithm to solve the problem. Our simulation results show that the proposed algorithm efficiently enables resource sharing in the given scenario with lower complexity, as compared to the Outage algorithm.

Cooperative communication with mobile relays is a promising technique to increase the performance of cellular users. However, mobile relays comes with the cost of unwillingness of a mobile user to act as relay and it needs to be intelligently selected to exploit the full benefits of cooperative communication. Relay selection is a well-investigated topic for static relays and these studies work with the objective of either maximizing the utility of relay and operator or relay and mobile users. None of the studies are considering the utilities of all three players (operator, mobile user and mobile relay) while selecting the relay. In this paper, we propose a centralized auction based relay selection algorithm taking into consideration the utilities of the three players involved in the scenario with best-worst selection criteria. We name the algorithm as Profitable Relay Selection Algorithm (PRSA). The result shows that mobile relay which provides fair utility gain is selected. PRSA shows higher utility gain compared to bidding game model.

Unlike traditional cellular networks, the multi-tier architecture of 5G networks supports Device-to-Device (D2D) communication, which allows devices to communicate with each other autonomously. Consequently, D2D-enabled Internet of Things (IoT) promise to provide higher bandwidth through frequency spectrum sharing. However, energy efficient operation of the network becomes a major challenge due to unattended operation of devices. In this study, we propose a solar energy harvesting based model for throughput maximization of an overlay in-band D2D network and consider an energy prediction model for optimal power management. Sum rate maximization problem subject to energy and power constraints is formulated for multiple D2D pairs and maximum achievable throughput is investigated by employing optimal power allocation at transmitters. The proposed model is analysed using real world solar energy harvesting data. The results show that consistently high throughput can be achieved by scheduling the energy arrival duration, while at the same time, energy inefficiency can also handled by optimal power allocation at the devices.

Mobile users in public vehicles (train, buses, etc.) suffer from low signal quality due to the enclosed nature of the vehicles. Small cells may be used to provide good signal quality in enclosed regions. Therefore, deployment of small cells in public vehicles can be a promising solution to the quality-of-service issue. However, deploying such moving small cells makes the resource allocation in the network a challenging problem, due to the time-varying interference experienced and created by them. In this paper, we solve the joint resource allocation (resource blocks and power) problem in a cellular network with moving small cells, with the objective of enhancing the quality-of-service in the network. To capture the effect of mobility on the conflict relationship of the users in the moving small cell network, we propose representing the interference relationship as a time interval dependent interference (TIDI) graph. We formulated a joint resource allocation problem, which is decomposed into the subproblems of resource block (RB) allocation for a fixed power allocation and transmission power allocation for a fixed RB allocation to make the problem tractable. We propose an iterative resource allocation algorithm (IRAA) to solve the joint resource allocation problem.

Relaying is a promising technique in cellular networks for improving system capacity and coverage. To facilitate the deployment of relays in remote areas without ready access to the electrical grid, energy-harvesting relays may be deployed. Energy-harvesting has been studied extensively for sensor networks, but it is still an open problem for cellular networks. In this paper, we study the problem of the joint allocation of orthogonal frequency division multiplexing access resource blocks and transmission power to users in a cellular network with energy-harvesting relays. The energy-harvesting process is stochastically described by a time-varying Poisson process. We propose a new metric called survival probability as the selection criteria for an energy-harvesting relay to support data transmissions. We propose a survival probability-based resource allocation (SPRA) algorithm. The algorithm solves the joint problem of resource block allocation, power control, and associating relays to users in a cellular network. We show the achievable data rates of SPRA for different energy harvesting rates.

Multihop communication has attracted a lot of attention as an effective transmission strategy for future cellular networks. It can be an effective technique to increase data rate and enhance coverage. However, multihop relaying with a single Radio Access Technology (RAT) may not be able to provide the flexibility required for wireless cellular network which has moved towards heterogeneity. Therefore, multihop cellular network needs to be integrated with multi-RAT to provide the required flexibility in the cellular architecture. When multihop cellular network is provided with multi-RAT capability, RAT selection for a certain transmission session becomes part of its resource management. RAT selection decision depends on the characteristics of the radio links and the availability of resources in the different RATs. A careful control of power between user and relay is required to exploit multi-RAT and multihop capabilities to their maximum potential. In this paper, we formulated a RAT selection and a transmission power control for a mobile user and relay using an indifference curve. We selected the RAT and allocated the power for a single users transmission such that the interference created by the mobile user and the relay is minimized and the achievable data rate is satisfied.

Passengers in city buses are possibly one of the main contributors to the increase in mobile traffic in cellular networks. Instead of each passenger accessing the cellular base station, we may deploy a small cell on the bus to serve these passengers. To reap the benefits of this deployment in the cellular network, resource allocation of the network needs to be addressed carefully. We call the small cells deployed in city buses as moving small cells. In this paper, we propose a probabilistic graph based resource allocation (PGRA) algorithm in a cellular network with moving small cells. We exploit the headway characteristics of city buses to study the interference relationship between different moving small cells. Our performance metric is the number of resource blocks used. Our performance evaluation shows an average decrease of 19.84% in the number of RBs used with moving small cells in the network as compared to a scenario without moving small cells, requiring each passenger to access the base station.

Improvement in signal quality and service quality by femtocells offers a natural opportunity for them to be deployed in vehicles. However, resource allocation with mobile femtocells becomes challenging due to the dynamic interference patterns as the mobile femtocells move. In this paper, we introduce the problem of allocating resources in a cellular network with mobile femtocells. We consider two types of femtocells in the scenario, a) fixed femtocells (deployed in stationary location e.g train stations); and b) mobile femtocell with deterministic mobility (e.g deployed on trains). We use the speed and path information of the mobile femtocells to determine the interference relationships between different femtocells at different time instants and represent it as a time interval dependent interference graph. Finally using the interference graph, we proposed a cluster-based resource allocation algorithm. Performance analysis shows that the allocated resources with mobile femtocell in the scenario are close to the ones without mobile femtocells and much less than a random allocation.

High data rates and multiple channel profiles of a MIMO system are naturally well-suited to carry video content. However, a video communication scheme to exploit these desirable properties of MIMO systems is widely unexplored. Even the most sophisticated MIMO rate adaptation methods rely on channel BER which shows a monotonic decrease as the channel quality improves. However, video quality, for which PSNR is a better measure than BER, does not show a proportional increase with improved channel quality because of error concealment. In this paper, we present a novel application-aware rate adaptation method which can detect variations in a MIMO channel at a video receiver, quantify the impact of these variations on the received video quality, and adaptively select a transmission profile (consisting of modulation, coding and MIMO mode) to provide unprecedented improvements in video quality. The proposed application-aware MIMO Video Rate Adaptation (MVRA) method relies on a comprehensive model of source- and channel-induced distortions in video quality. Using this model, a MIMO receiver can select an appropriate transmission profile on a per-GOP basis. Trace-driven evaluations over an 802.11n channel show that the proposed MVRA method's PSNR is very close to the PSNR of an optimal RA scheme. Comparison with state-of-the-art MIMO RA methods shows that the proposed MVRA method provides consistently better PSNR, with an average improvement of 19.29% (5.5486 dBs) over the best existing MIMO RA scheme.

In this paper, we study the impact of spatio-temporal variations of a video sequence on wireless MIMO video transmission. In the light of our findings, we propose a cross-layer MIMO video communication architecture for efficient multimedia communication. Existing methods complement Spatial Multiplexing (SM) and Space Time Block Coding (STBC) with enhanced source and channel coding strategies to improve video PSNR at a MIMO receiver. However, these methods ignore the adverse impacts of spatio-temporal content variations on video quality and decoding complexity. We show that the decision to switch between SM and STBC for effective video communication must be based on both channel conditions and motion intensity of a video. We quantify a video's spatio-temporal characteristics using a motion intensity measure, and propose a simple motion-adaptive MIMO video communication technique which can judiciously switch between SM and STBC configurations. Experimental evaluations show that the proposed technique provides significantly better video quality while having very low decoding complexity at the receiver. Based on this technique, we propose a novel cross-layer motion-adaptive architecture for wireless video communication.