To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper, we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used to characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively kept below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

This paper aims at designing high-data-rate swarm UAV networks with distributed beamforming capabilities. The primary challenge is that the beamforming gain in swarm UAV networks is highly affected by the UAVs’ flight altitude, their movements and the resulting intermittent link blockages, as well as the availability of channel state information (CSI) at individual UAVs. To address this challenge, we propose FlyBeam, a learning- based framework for joint flight and beamforming control in swarm UAV networks. We first present a mathematical formulation of the control problem with the objective of maximizing the throughput of swarm UAV networks by jointly controlling the flight and distributed beamforming of UAVs. Then, a distributed solution algorithm is designed based on a combination of Echo State Network learning and online reinforcement learning. The former is adopted to approximate the utility function for individual UAVs based on online measurements, by jointly considering the unknown blockage dynamics and other factors that affect the beamforming gain. The latter is used to guide the exploitation and exploration in FlyBeam. The effectiveness of FlyBeam is evaluated through an extensive simulation campaign. Results indicate that significant (up to 450%) beamforming gain can be achieved by FlyBeam. We also investigate the effects of blockages and UAV flight altitude on the beamforming gain. It is found that, which is somewhat surprising, higher (rather than lower) beamforming gain can be achieved by FlyBeam with denser blockages in swarm UAV networks.

Existing routing protocols may not be applicable in UAV networks because of their dynamic network topology and lack of accurate position information. In this paper, an enhanced flooding-based routing protocol is designed based on random network coding (RNC) and clustering for swarm UAV networks, enabling the efficient routing process without any routing path discovery or network topology information. RNC can naturally accelerate the routing process, with which in some hops fewer generations need to be transmitted. To address the issue of numerous hops and further expedite routing process, a clustering method is leveraged, where UAV networks are partitioned into multiple clusters and generations are only flooded from representatives of each cluster rather than flooded from each UAV. By this way, the amount of hops can be significantly reduced. The technical details of the introduced routing protocol are designed. Moreover, to capture the dynamic network topology, the Poisson cluster process is employed to model UAV networks. Afterwards, stochastic geometry tools are utilized to derive the distance distribution between two random selected UAVs and analytically evaluate performance. Extensive simulation studies are conducted to prove the validation of performance analysis, demonstrate the effectiveness of our designed routing protocol, and reveal its design insight.

To mitigate the long-term spectrum crunch problem, the FCC recently opened up the 6 GHz frequency band for unlicensed use. However, the existing spectrum sharing strategies cannot support the operation of access points in moving vehicles such as cars and UAVs. This is primarily because of the directionality-based spectrum sharing among the incumbent systems in this band and the high mobility of the moving vehicles, which together make it challenging to control the cross-system interference. In this paper we propose SwarmShare, a mobility-resilient spectrum sharing framework for swarm UAV networking in the 6 GHz band. We first present a mathematical formulation of the SwarmShare problem, where the objective is to maximize the spectral efficiency of the UAV network by jointly controlling the flight and transmission power of the UAVs and their association with the ground users, under the interference constraints of the incumbent system. We find that there are no closed-form mathematical models that can be used characterize the statistical behaviors of the aggregate interference from the UAVs to the incumbent system. Then we propose a data-driven three-phase spectrum sharing approach, including Initial Power Enforcement, Offline-dataset Guided Online Power Adaptation, and Reinforcement Learning-based UAV Optimization. We validate the effectiveness of SwarmShare through an extensive simulation campaign. Results indicate that, based on SwarmShare, the aggregate interference from the UAVs to the incumbent system can be effectively controlled below the target level without requiring the real-time cross-system channel state information. The mobility resilience of SwarmShare is also validated in coexisting networks with no precise UAV location information.

Networks of Unmanned Aerial Vehicles (UAVs), composed of hundreds, possibly thousands of highly mobile and wirelessly connected flying drones will play a vital role in future Internet of Things (IoT) and 5G networks. However, how to control UAV networks in an automated and scalable fashion in distributed, interference-prone, and potentially adversarial environments is still an open research problem. This article introduces SwarmControl, a new software-defined control framework for UAV wireless networks based on distributed optimization principles. In essence, SwarmControl provides the Network Operator (NO) with a unified centralized abstraction of the networking and flight control functionalities. High-level control directives are then automatically decomposed and converted into distributed network control actions that are executed through programmable software-radio protocol stacks. SwarmControl (i) constructs a network control problem representation of the directives of the NO; (ii) decomposes it into a set of distributed sub-problems; and (iii) automatically generates numerical solution algorithms to be executed at individual UAVs.We present a prototype of an SDR-based, fully reconfigurable UAV network platform that implements the proposed control framework, based on which we assess the effectiveness and flexibility of SwarmControl with extensive flight experiments. Results indicate that the SwarmControl framework enables swift reconfiguration of the network control functionalities, and it can achieve an average throughput gain of 159% compared to the state-of-the-art solutions.

The emergence of multi-beam directional antennas (MBDAs) has paved the way for fast and high-throughput data communications by providing concurrent multi-directional transmissions. However, the existing routing protocols are not capable of utilizing the advantages of MBDAs. In this paper, we have developed a new routing scheme, called volcano routing, which can exploit the concurrent packet dispatching capability of MBDAs for high-throughput data delivery. Its topology resembles the flow of volcano lava and several routing “pipes” are used, which can detour around the network “holes” or blocked areas. The routing process consists of two phases: 1) Main path search phase: There is a main path at the core of each pipe. Multiple optimal main paths are formed that have a high potential of adding side nodes to enable multi-beam communications. A hierarchical scoring system and the performance metrics are used to evaluate the quality of the main paths. 2) Volcano establishment phase: The top-quality main paths are selected, and side paths are formed around each main path to establish the volcano pipes. A multi-beam traffic scheduling and dispatching policy is also proposed to achieve better performance. Our results show that the volcano routing scheme can exploit the advantages of MBDAs for achieving high data rates.

This article studies distributed algorithms to control self-organizing flying drones with massive MIMO networking capabilities - a network scenario referred to as mDroneNet. We attempt to answer the following fundamental question: what is the optimal way to provide spectrally-efficient wireless access to a multitude of ground nodes with mobile hotspots mounted on drones and endowed with a large number of antennas; when we can control the position of the drone hotspots, the association between the ground users and the drone hotspots, as well as the pilot sequence assignment and transmit power for the ground users? To the best of our knowledge, this is the first time that massive MIMO capabilities are considered in self-organizing flying drone networks. We first derive a mathematical formulation of the problem of joint power, association and movement control in mDroneNet, with the objective of maximizing the aggregate spectral efficiency of the ground users. It is shown that the resulting network control problem is a mixed integer nonlinear nonconvex programming (MINLP) problem. Then, a distributed solution algorithm with polynomial time complexity is designed by solving three closely-coupled subproblems: access association, joint pilot sequence assignment and power control, and drone movement control. As a performance benchmark, a globally-optimal but centralized solution algorithm is also designed based on a combination of the branch and bound framework and convex relaxation techniques. Results indicate that the distributed solution algorithm converges fast (within tens of iterations) and achieves a network spectral efficiency very close to the global optimum obtained by the centralized solution algorithm (over 90% in average).

Spatial spectrum sensing (SSS) enables mobile devices to sense the spatial spectrum holes and reuse the scarce spectrum opportunistically. In this paper, we model and analyze the SSS in uplink two-tier user-centric deployed heterogeneous networks (HetNets) where secondary users (SUs) sense the spectrum holes of cellular users. In the two-tier user-centric deployed HetNets, small cell base stations (SBSs) are deployed in hotspots with high user density, and macro base stations (MBSs) are deployed uniformly. Based on the semi-static power control mechanism, the average transmit power of cellular users associated with MBS and SBS are derived, respectively. Furthermore, the spatial false alarm probability and the spatial miss detection probability of a typical SU are obtained, respectively. Moreover, we characterize the coverage probability and the area spectral efficiency (ASE) of SU and cellular networks. The SUs' optimal SSS radius is obtained to maximize the ASE of the entire network while guaranteeing the ASE of cellular networks above a certain threshold. Simulation results show that when the density of SUs is small, a decrease in SUs' SSS radius reduces the coverage probability of SUs. However, it improves the ASE of SUs networks, although the inter-SU interference increases.

Unmanned aerial vehicles (UAVs) are becoming important communication infrastructures. One major challenge of communications with UAV networks is the routing protocol design. Due to the inherent characteristics (e.g., dynamic network topology and limited UAV device capabilities), it is difficult to directly apply existing routing protocols that utilize network topology information and routing path explorations. In this article, two novel routing protocols are designed based on random network coding (RNC) for a swarm UAV network, where UAVs operate cooperatively as a swarm, enabling efficient routing process. The first routing protocol utilizes the unique feature of RNC: Original packets can be decoded as long as an UAV accumulates sufficient generations. This property can be used to effectively expedite the underlying routing process. The second routing protocol further improves the efficiency where each forwarding UAV only needs to create a new generation rather than decoding original packets. Accordingly, the duration of each hop can be significantly reduced. Extensive simulations have been conducted to evaluate the performance of the designed routing protocols. The simulation results demonstrate that our designed routing protocols can effectively enhance the performance on both average transmission delay and delay violation probabilities compared to benchmark methods.

This paper develops a novel framework for the modeling and analysis of spatial spectrum sensing (SSS) for device-to-device (D2D) communications in uplink two- tier user-centric deployed heterogeneous networks (HetNets), where small cell base stations (SBSs) are deployed in the places with high user density termed hotspots introduced by 3GPP. We study the average transmit power of uplink users, the probability of spatial false alarm and the probability of spatial miss detection of a typical D2D transmitter (D2D-Tx) during SSS. Based on the results, we further characterize the coverage probability of a typical D2D user and the area spectral efficiency (ASE) of D2D networks. Simulation results verify our analysis and demonstrate the advantages of SSS-based D2D communications in future wireless networks.

Routing protocol design is one of the major challenges for swarm UAV networks. Due to the characteristics of a dynamic network topology, the low-complexity and the large volume of UAV devices, existing routing protocols based on network topology information, and routing table updates are not applicable in swarm UAV networks. In this paper, a Random Network Coding (RNC) enabled routing protocol is proposed to support an efficient routing process, which does not require network topology information or pre-determined routing tables. With the proposed routing protocol, the routing process could be significantly expedited, since each forwarding UAV may have already overheard some encoded packets in previous hops. As a result, some hops may be required to deliver a few encoded packets, and less hops may need to be completed in the whole routing process. The corresponding simulation study is conducted, demonstrating that our proposed routing protocol is able to facilitate a more efficient routing process.

Recent research advances in wireless security have shown that advanced jamming can significantly decrease the performance of wireless communications. In advanced jamming, the adversary intentionally concentrates the available energy budget on specific critical components (e.g., pilot symbols, acknowledgement packets, etc.) to (i) increase the jamming effectiveness, as more targets can be jammed with the same energy budget; and (ii) decrease the likelihood of being detected, as the channel is jammed for a shorter period of time. These key aspects make advanced jamming very stealthy yet exceptionally effective in practical scenarios. One of the fundamental challenges in designing defense mechanisms against an advanced jammer is understanding which jamming strategies yields the lowest throughput, for a given channel condition and a given amount of energy. To the best of our knowledge, this problem still remains unsolved, as an analytic model to quantitatively compare advanced jamming schemes is still missing in existing literature. To fill this gap, in this paper we conduct a comparative analysis of several most viable advanced jamming schemes in the widely-used MIMO networks. We first mathematically model a number of advanced jamming schemes at the signal processing level, so that a quantitative relationship between the jamming energy and the jamming effect is established. Based on the model, theorems are derived on the optimal advanced jamming scheme for an arbitrary channel condition. The theoretical findings are validated through extensive simulations and experiments on a 5-radio 2x2 MIMO testbed. Our results show that the theorems are able to predict jamming efficiency with high accuracy. Moreover, to further demonstrate that the theoretical findings are applicable to address crucial real-world jamming problems, we show that the theorems can be incorporated to state-of-art reinforcement-learning based jamming algorithms and boost the action exploration phase so that a faster convergence is achieved.

In many environments, unmanned aerial vehicle (UAV) swarms are often used for visual data collection. In these scenarios, each element of the swarm collects a single image and those images would be put together into a single data product. However, while small UAV platforms are ideal for imagery collection, they have a very difficult time exfiltrating this data from the data collection scene to a larger data aggregation platform. In this paper, we introduce the Application-Based Rate Control for Emerging Systems (ARES). ARES is a rate control algorithm specifically designed to prioritize traffic that contains more information over traffic with less information. The novelty of ARES is that it uses traditional distributed transport-layer concepts in order to develop a realistically implementable and fully distributed intelligent rate control algorithm. The algorithm is evaluated in MATLAB, and is shown to far outperform traditional congestion control algorithms in terms of data collection efficiency.

Wireless networks are extremely vulnerable to a plethora of security threats, including eavesdropping, jamming, and spoofing, to name a few. Recently, a number of next-generation cross-layer attacks have been unveiled, which leverage small changes on one network layer to stealthily and significantly compromise another target layer. Since cross-layer attacks are stealthy, dynamic, and unpredictable in nature, novel security techniques are needed. Since models of the environment and attacker's behavior may be hard to obtain in practical scenarios, machine learning techniques become the ideal choice to tackle cross-layer attacks. In this paper, we propose FORMAT, a novel framework to tackle cross-layer security attacks in wireless networks. FORMAT is based on Bayesian learning and made up by a detection and a mitigation component. On one hand, the attack detection component constructs a model of observed evidence to identify stealthy attack activities. On the other hand, the mitigation component uses optimization theory to achieve the desired trade-off between security and performance. The proposed FORMAT framework has been extensively evaluated and compared with existing work by simulations and experiments obtained with a real-world testbed made up by Ettus Universal Software Radio Peripheral (USRP) radios. Results demonstrate the effectiveness of the proposed methodology as FORMAT is able to effectively detect and mitigate the considered cross-layer attacks.

This paper introduces a congestion control protocol for lossy wireless networks. The Waveform Independent congestion control Protocol (WIP) is a congestion control protocol designed to work well in extremely lossy wireless networks. It accomplishes this by comparing the packet transmit rate and the receive capacity. The difference between these two values is then used to determine whether there is congestion in the network. WIP can integrate into many existing protocols, as it only defines a change in the congestion indication technique and is independent of the window evolution and delivery guarantee transport layer functionality. We test this protocol in simulation and compare it to traditional loss-based congestion indication and demonstrate that it performs far better in lossy networks.

This paper presents a transport layer protocol for multi-path networks with blockage. Using urban SATCOM as an example, we see from data taken from a 2006 measurement campaign that these blockages are generally on the order of 1 -- 5seconds in length and the links are blocked approximately 33%of the time. To compensate for this type of impairment, we have developed a multipath IP overlay routing algorithm, a random linear coding reliability scheme, and a maximum-queue-based (MQCC) congestion control algorithm. MQCC uses average buffer occupancy as a measure of the congestion in a network (as opposed to packet loss or round trip time (RTT)) and updates the transmission rate of each source to avoid network congestion. This allows us to design a congestion control algorithm that is independent of the channel conditions and can be made resilient to channel losses. The reliability scheme uses selective negative acknowledgments (Snacks) to guarantee packet delivery to the destination. We show through simulation that we can approach the optimal benchmark in realistic loss blockage channels.

Blockage of communication links due to environmental obstructions and the resulting on/off channel behavior present challenges for providing reliable communication in mobile wireless networks. Traditional reliability techniques such as forward error correction and automatic repeat request (ARQ) are not designed to operate at the timescales typically observed in blockage channels. This work presents an approach to overcoming blockage that consists of multipath routing coupled with end-to-end rateless coding. In order to provide compatibility with operational IP networks as well as the possibility of incremental deployment, these techniques can be implemented as a routing overlay. We present algorithms to compute the maximum throughput achievable through this routing overlay approach, as well as a specific scheme to implement a multipath routing overlay in an IP network. Finally, we provide results from testing this approach on a mobile wireless network with satellite communication links. In our test scenario, satellite links suffer blockage from buildings in an urban setting. Our results characterize multipath routing overlay performance, which depends on the responsiveness of the underlay routing scheme.

This work addresses the challenge in providing reliable communication in mobile networks with intermittent, on/off links through the use of a routing overlay designed to deal with these channel impairments. Link blockage is a predominant feature of mobile networks operating at 10+ GHz frequencies, and current techniques are ill-suited to address this problem. We present an approach comprised of multiple-path routing with end-to-end coding, queue-length-based congestion control, and a negative acknowledgement (NACK) loss-recovery scheme; these are implemented in an IP-overlay. The demonstrated scenario consists of two clusters of mobile ground vehicles operating in an ``urban canyon'' environment. Blockage occurs on satellite links connecting the clusters. Through the use of interactive displays, demo participants gain an understanding of routing behavior.

Video streaming applications are becoming increasingly popular as low priced video-enabled mobile devices (such as smart phones) become more common. However, traditional video streaming systems are not designed for mobile devices, and require both high computational complexity at the video sensor and very high channel quality to achieve good performance. Our recently proposed compressive video sensing (CVS) video streaming system is a low complexity, low power compressed-sensing-based encoder designed to address these challenges. However, even using CVS, the energy consumption of multimedia sensors is still much higher than that of traditional scalar sensors. In this article, we present a cooperative relay-assisted compressed video sensing (RA-CVS) system that takes advantage of the error resilience of video encoded using CVS to maintain good video quality at the receiver while significantly reducing the required SNR, and therefore the required transmission power at the multimedia sensor node. This system uses the natural error resilience of CS encoded video signals to design a cooperative scheme that directly reduces the mean squared error (MSE) of the reconstructed CS samples representing a video frame, which allows the receiver to correctly reconstruct the video even at very low SNR levels. The proposed system is tested using both simulation and USRP2 testbed evaluation and is shown to outperform traditional cooperative systems in terms of received video quality as a function of channel SNR.

Real-time encoding and error-resilient wireless transmission of multimedia content using traditional encoding techniques requires relatively high processing and transmission power, while pervasive surveillance and monitoring systems often referred to as wireless multimedia sensor networks (WMSNs) are generally composed of low-power, low-complexity devices. To bridge this gap, this article introduces and analyzes a compressive video sensing (CVS) encoder designed to reduce the required energy and computational complexity at the source node. The proposed encoder leverages the properties of compressed sensing (CS) to overcome many of the limitations of traditional encoding techniques, specifically lack of resilience to channel errors, and high computational complexity. Recognizing the inadequacy of traditional rate-distortion analysis to account for the constraints introduced by resource-limited devices, we introduce the notion of rate-energy-distortion, based on which we develop an analytical/empirical model that predicts the received video quality when the overall energy available for both encoding and transmission of each frame of a video is fixed and limited and the transmissions are affected by channel errors. The model allows comparing the received video quality, computation time, and energy consumption per frame of different wireless streaming systems, and can be used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. Based on the proposed model, we show that the CVS video encoder outperforms (in an energy constrained system) two common encoders suitable for a wireless multimedia sensor network environment; H.264/AVC intra and motion JPEG (MJPEG). Extensive results show that CVS is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

This paper presents the design of a networked system for joint compression, rate control and error correction of video over resource-constrained embedded devices based on the theory of Compressed Sensing (CS). The objective of this work is to design a cross-layer system that jointly controls the video encoding rate, the transmission rate, and the channel coding rate to maximize the received video quality. First, compressed sensing-based video encoding for transmission over Wireless Multimedia Sensor Networks (WMSNs) is studied. It is shown that compressed sensing can overcome many of the current problems of video over WMSNs, primarily encoder complexity and low resiliency to channel errors. A rate controller is then developed with the objective of maintaining fairness among different videos while maximizing the received video quality. It is shown that the rate of Compressed Sensed Video (CSV) can be predictably controlled by varying only the compressed sensing sampling rate. It is then shown that the developed rate controller can be interpreted as the iterative solution to a convex optimization problem representing the optimization of the rate allocation across the network. The error resiliency properties of compressed sensed images and videos are then studied, and an optimal error detection and correction scheme is presented for video transmission over lossy channels. Finally, the entire system is evaluated through simulation and test bed evaluation. The rate controller is shown to outperform existing TCP-friendly rate control schemes in terms of both fairness and received video quality. The test bed results show that the rates converge to stable values in real channels.

Real-time encoding and error-resilient wireless transmission of multimedia content require high processing and transmission power. This paper investigates the rate-distortion performance of video transmission over lossy wireless links for low-complexity multimedia sensing devices with a limited budget of available energy per video frame. An analytical/empirical model is developed to determine the received video quality when the overall energy allowed for both encoding and transmitting each frame of a video is fixed and the received data is affected by channel errors. The model is used to compare the received video quality, computation time, and energy consumption per frame of different wireless streaming systems. Furthermore, it is used to determine the optimal allocation of encoded video rate and channel encoding rate for a given available energy budget. The proposed model is then applied to compare the energy-constrained wireless streaming performance of three encoders suitable for a wireless multimedia sensor network environment; H.264, motion JPEG (MJPEG) and our recently developed compressed sensing video encoder (CSV). Extensive results show that CSV, thanks to its low complexity, and to a video representation that is inherently resilient to channel errors, is able to deliver video at good quality (an SSIM value of 0.8) through lossy wireless networks with lower energy consumption per frame than competing encoders.

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to study performance limits and outline key design principles that will be the basis for cross-layer protocol stacks for efficient transport of compressive video streams. Hence, this paper investigates the effect of key video parameters (i.e., quantization, CS samples per frame, and channel encoding rate) on the received video quality of CS images transmitted through a wireless channels. It is shown that, unlike JPEG-encoded images, CS-encoded images exhibit an inherent resiliency to channel errors, caused by the unstructured image representation; this leads to basically zero loss in image quality for random channel bit error rates as high as 10-4, and low degradation up to 10-3. Furthermore, it is shown how, unlike traditional wireless imaging systems, forward error correction is not beneficial for wireless transmission of CS images. Instead, an adaptive parity scheme that drops samples in error is proposed and shown to improve image quality. Finally, we present our initial investigations on a low-complexity, adaptive video encoder that performs low-complexity motion estimation.

This paper investigates the potential of the compressed sensing (CS) paradigm for video streaming in Wireless Multimedia Sensor Networks. The objective is to co-design a low-complexity video encoder based on compressed sensing and a rate-adaptive streaming protocol for wireless video transmission. The proposed rate control scheme is designed with the objectives to maximize the received video quality at the receiver and to prevent network congestion while maintaining fairness between multiple video transmissions. Video distortion is represented through analytical and empirical models and minimized based on a new cross-layer control algorithm that jointly regulates the video encoding rate and the channel coding rate at the physical layer based on the estimated channel quality. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. The proposed scheme is shown to outperform TCP-Friendly Rate Control (TFRC).

The availability of inexpensive CMOS cameras and microphones that can ubiquitously capture multimedia content from the environment is fostering the development of Wireless Multimedia Sensor Networks (WMSNs), i.e., distributed systems of wirelessly networked devices that can retrieve video and audio streams, still images, and scalar sensor data. WMSNs require the sensor network paradigm to be re-thought in view of the need for mechanisms to deliver multimedia content with a pre-defined level of quality of service (QoS). A new rate control scheme for WMSNs is introduced in this paper with a two-fold objective: i) maximize the video quality of each individual video stream; ii) maintain fairness in video quality between different video streams. The rate control scheme is based on both analytical and empirical models and consists of a new cross-layer control algorithm that jointly regulates the end-to-end data rate, the video quality, and the strength of the channel coding at the physical layer. The end-to-end data rate is regulated to avoid congestion while maintaining fairness in the domain of video quality rather than data rate. Once the end-to-end data rate has been determined, the sender adjusts the video encoder rate and the channel encoder rate based on the overall rate and the current channel quality, with the objective of minimizing the distortion of the received video. Simulations show that the proposed algorithm considerably improves the received video quality without sacrificing fairness.

A proactive routing protocol called Multi-Mesh Tree (MMT) was developed for use in wireless ad hoc network to extend connectivity from an Internet gateway to around 20 mobile nodes in a city area. In the work presented here, we extend MMT to wireless ad hoc networks of around one hundred nodes through a clustering algorithm that is integrated into the MMT creation. The proposed scheme uses a hybrid approach, where the proactive MMT is used for intra cluster routing while a reactive MMT (RMMT) introduced in this article is used for inter cluster routing. We further propose a novel route discovery and route recording scheme using route request and route response messages but has low flooding overheads and exhibits high route stability under high node mobility conditions. We apply the proposed RMMT scheme to provide connectivity among moving teams of ground troops and present simulation results based on a study of this scenario.