Gunshot Detection Systems (GDS) are increasingly deployed in public, institutional, and protected areas to enable rapid, real-time response to firearm incidents. Conventional acoustic-only solutions suffer from high false-alarm rates, limited adaptability to dynamic environments, and restricted spatial awareness. Recent advances in Machine Learning (ML), Deep Learning (DL), and Internet of Things (IoT) technologies have enabled intelligent GDS capable of context-aware detection, firearm classification, and shooter localization through multi-modal inputs including audio, video, and infrared data. These systems can adapt to background noise, time-of-day conditions, and deployment context, improving detection reliability while reducing latency. This survey focuses on sensor-driven, IoT-enabled gunshot detection pipelines that involve on-site sensing, edge or cloud-based inference, and operational deployment, and it does not address internet-only information collection or general crime analytics frameworks. This survey analyzes 156 peer-reviewed studies published between 1998 and 2025, organized into six analytical dimensions: detection and modeling approaches, application contexts, hardware and deployment infrastructure, communication and security frameworks, environmental and spatial adaptability, and performance evaluation. Across these, we identify persistent challenges spanning both technical and operational domains, including accurate detection in noisy or reverberant environments, reliable localization in GPS-poor or complex terrains, low-latency multimodal fusion, scalability under resource constraints, dataset scarcity, privacy and ethical considerations, vulnerability to spoofing or suppressors, and secure, real-time communication for rapid response. By consolidating technical advances and mapping them to these open problems, the review provides actionable insights for designing scalable, energy-efficient, and trustworthy GDS suitable for diverse real-world deployments.

Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of industrial data have been generated at the network edge, driving the evolution of edge machine learning (ML). But during the ML model training, it may bring privacy leakage by traditional central methods. To address this issue, federated learning (FL) has been proposed as a distributed learning framework for training a global model without uploading raw data to protect data privacy. Since the communication and computing resources are usually limited in IIoT networks, how to reasonably select device and allocate bandwidth is crucial for the FL model training. Therefore, this article proposes a joint edge device selection and bandwidth allocation scheme for FL to minimize the time-averaged cost under the given long-term energy budget and delay constraints in the IIoT system. To tackle with this long-term optimization problem, we construct a virtual energy deficit queue and leverage the Lyapunov optimization theory to transform it into a list of round-wise drift-plus-cost minimization problems first. Then, we design an iterative algorithm to allocate reasonable bandwidth and select appropriate devices to achieve cost minimization while satisfying the energy consumption constraints. Besides, we develop an optimality analysis of the average cost and energy violation for our proposed scheme. Extensive experiments verify that our proposed scheme can achieve superior performance in cost efficiency over other schemes while guaranteeing FL training performance.

With the development of computation-intensive applications, unmanned aerial vehicle (UAV)-enabled multi-access edge computing (MEC) provides task offloading service for the users with or without terrestrial infrastructure support. Meanwhile, the next generation of UAVs communication systems are expected to be user-centric. Therefore, more attention should be paid to users’ satisfaction with the offered service. In this paper, we study the service satisfaction-oriented task offloading and UAV scheduling problem for UAV-enabled MEC networks, where the task priorities are considered based on the delay requirements of users’ tasks and the remaining energy status of users. Specifically, we firstly divide the users into different groups via the K-means-based grouping algorithm. Then, we develop a novel user satisfaction model by jointly considering the task processing delay and energy saving, based on which a total user satisfaction maximization problem is formulated to jointly optimize the task offloading decisions and UAV scheduling strategy. To solve the formulated problem, we decompose it into two sub-problems, i.e., the UAV scheduling sub-problem and the task offloading sub-problem. To solve the first sub-problem, we develop a genetic algorithm (GA)-based UAV scheduling algorithm through dealing with multiple balanced assignment problems. To address the second sub-problem, a GA-based task offloading algorithm is developed. Then, we propose a joint task offloading and UAV scheduling optimization algorithm to solve the original optimization problem. Finally, simulation results demonstrate that the proposed optimization algorithm not only converges fast, but also improves the total users’ satisfaction greatly. The total users’ satisfaction improved by the proposed scheme is up to 16.9% in the case of 170 users.

Vehicle-to-vehicle communications can offer direct information interaction, including security-centered information and entertainment information. However, the rapid proliferation of vehicles and the diversity of communications services demand for a more intelligent and efficient resource allocation framework to enhance network performance. In this article, a multi-agent deep reinforcement learning-based resource allocation framework is developed to jointly optimize the channel allocation and power control to satisfy the heterogeneous Quality-of-Service (QoS) requirements in heterogeneous vehicular networks. In the proposed framework, the utility maximization problem is formulated by considering two types of traffics, i.e., the strict ultrareliable and low-latency requirements for safety-centric applications and the high-capacity requirements for entertainment applications. The utility of each vehicular users is formulated as a multicriterion objective function by taking into account the heterogeneous traffic requirements. To overcome the drawbacks of the traditional totally centralized and distributed deep reinforcement learning-based resource allocation approaches, we propose a multi-agent deep deterministic policy gradient algorithm with centralized learning and decentralized execution to solve the formulated optimization problem. The normalization of the input states and reward functions is introduced to speed up the training and learning progress of the proposed algorithm. Simulation results show the superiority of the proposed algorithm in terms of the convergence and system performance through the comparison with the other methods and schemes for the delay-sensitive applications and delay-tolerant applications.

Game engines are effective in generating and visualizing digital twins of urban environment in real time. However, current game engines are not built to handle the influx of real time data streams from a diverse array of IoT devices, nor can they render a real-time dynamic mesh streamed from a scanning device such as a Light Detection and Ranging (LIDAR) system. In this paper, a combination of pre-processing and post-processing techniques are considered in variably processed batches of point-cloud data. Additionally, we propose a quantitative error analysis for points generated on our experimental aerial mapping platform, as well as an analysis for the accuracy improvement after post-processing. Experimental results show that the proposed rendering algorithm and post-processing could enable a game engine to efficiently generate a highly accurate digital twin of urban environment.

Ground penetrating radars (GPRs) carried by mobile platforms, such as vehicles and drones, have been applied in various applications, for instance, subsurface utility detection, structural health inspection, and autonomous driving. However, existing GPR systems are not able to operate autonomously and adaptively due to several challenges, including the lack of intelligence, uncertain and dynamic nature of sensing environments, and huge state and action spaces. To overcome these challenges, in this article, we propose an autonomous cognitive GPR (AC-GPR) enabled by a deep reinforcement learning (DRL) approach. Specifically, the operation of the proposed AC-GPR is first formulated as a sequential decision process. A novel reward function is developed for the DRL model by defining and combining two different types of entropy-based rewards resulting from object detection and recognition, respectively. A deep Q-learning network (DQN) is developed to address the extreme curse of dimensionality in the state space and learn a policy directing the actions of the AC-GPR. The AC-GPR is evaluated using software called GprMax by combining DRL with GPR modeling and simulation. Results show that our proposed DRL-based AC-GPR outperforms other GPR systems using different approaches in terms of detection accuracy and operating time.

Game engines are ideal platforms to generate and visualize digital twins of smart cities in real time. The real-time mapping of a smart city faces two challenges: (1) streaming data from Internet of Things (IoT) devices, (2) rendering a high-throughput and heterogeneous digital twin. Current game engine infrastructure is not built to handle the influx of real time data streams from a diverse array of IoT devices, nor can they render a real-time dynamic mesh streamed from a scanning device such as a LIDAR. As meshes are the basic framework needed to render digital twin objects that represent their real word counterparts and real time IoT streams are necessary for modeling an accurate digital twin, both of these issues must be resolved in order to use game engines to create a digital twin of a smart city. In this paper, we propose a networking infrastructure capable of handling a wide variety of IoT devices and a novel mesh rendering algorithm. Additionally, we provide a quantitative error analysis. Experimental results show that the proposed streaming method and rendering algorithm could enable a game engine to efficiently generate a digital twin of a smart city.

With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing.

The medium access probability (MAP) of a random access protocol can severely impact network throughput especially for delay-sensitive applications, since it determines whether a node should transmit packets in a given slot or not. This paper focuses on network throughput maximization through optimizing the MAPs of all users under delay quality-of-service constraints in wireless interference-limited networks. Specifically, first, the total delay for transmitting one packet for a user is analyzed and derived based on an M/G/1 model. Then, the stochastic property of the aggregated interference is analyzed and its distribution is modeled as a log-normal distribution. Based on the delay and interference models, an optimization problem is formulated to derive the optimal MAPs so that the network throughput is maximized under the delay constraints. Two network traffic scenarios, homogeneous and heterogeneous user traffic, are discussed, respectively. For the case of homogeneous traffic, a closed-form expression of the optimal MAP is derived; for the case of heterogeneous traffic, a global E-optimal algorithm based on the branch-and-bound framework and convex relaxation technology is proposed with relatively low complexity. Simulations results show that the proposed algorithms can achieve superior network throughput performance over existing schemes.

This paper develops the traffic models of smart grid electronic data (E data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of E data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data's sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

Spatial modulation (SM) conveys information bits by utilizing both the antenna index and complex symbols to form a 3-D constellation. Similar to 2-D modulation, the structure of 3-D constellation could greatly affect the transmission reliability. In this paper, a generalized 3-D constellation design is proposed to optimize the constellation diagram used for each antenna, i.e., to optimize the complex symbols and their total number for each antenna and finally to enhance the transmission reliability. The optimal design method with exhaustive search algorithm may cause prohibitive computational complexity, especially when the cardinality of 3-D constellation is large. To overcome this issue, a recursive design algorithm is proposed with a computational complexity increasing polynomially with the cardinality of 3-D constellation. Extensions of the proposed methods to the SM constellation design for massive multiple-input multiple-output transmission, generalized spatial modulation (GSM) constellation design, and the SM constellation design with transmit antenna correlation are also discussed. Simulations are done to validate those theoretical analysis, and results show that the proposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSM systems without constraints on the number of transceiver antennas. It is also shown that the proposed approach offers better symbol-error-rate performance than other solutions.

To relieve the current overload of cellular networks caused by the continuously growing multimedia service, mobile edge collaboration, which exploits edge users to distribute videos for base station (BS), provides an effective way to share the heavy BS load. With the emergence of mobile edge technologies for Internet-of-Things applications, such as device to device and machine to machine, how to exploit users' social characteristics and mobility to minimize the number of transmissions of BS and how to improve the quality of experience of users have become the key challenges. In this paper, we study two aspects that are critical to these issues. One is the two-step detection mechanism, namely the establishment of virtual communities and collaborative clusters. Specifically, we take into consideration user preference for content and location. First of all, a virtual community is established, which exploits the coalition game based on the user's preference list to dynamically divide users into multiple communities. Then, to take full advantage of the temporary link established between users, a grid-based clustering method is proposed to manage the video requesting users. On the other hand, we propose a scalable video coding sharing scheme based on user's social attributes. This approach makes video distribution more flexible at the edge of mobile network through collaboration among users, and effectively reduces transmission energy consumption of transmitters. Numerical results show that the proposed mechanism can not only effectively alleviate the BS load, but also dramatically improve the reliability and adaptability of video distribution.

To offload and alleviate the heavy base station (BS) traffic load caused by the rapidly growing video services, device-to-device (D2D) communication, as one of the most indispensable technologies of the future cellular networks, can be potentially exploited by mobile users to distribute videos for a BS. In this paper, an effective pricing-based multicast video distribution system and a grid-based clustering method are proposed to support the distribution. Moreover, with the consideration of users' mobility and social characteristics, we classify them into multicast and core types by studying the user stay probability and familiarity. In particular, core users can cooperate with the BS to distribute videos to the multicast users through intracluster D2D multicast. However, core users cannot selflessly help the BS to distribute videos; instead, they will evaluate their personal benefits before distributing the videos to the multicast users. Further, a Stackelberg game-based pricing mechanism is proposed to inspire the core users to distribute videos. Simulation results demonstrate that the proposed mechanism can not only effectively alleviate the BS traffic load, but also significantly improve the effectiveness and reliability of video transmission.

The IEEE 1609.4 standard suggests that the control channel (CCH) should be devoted to the beacon delivery, where the multiple access control (MAC) plays an important role in scheduling the network resources to multiple nodes. This paper proposes a novel OFDM-based group contention MAC (OGCMAC) for vehicular ad hoc networks (VANETs), in which a new CCH architecture, a modified multi-carrier burst contention (MCBC) and a group contention strategy are established. The CCH architecture is proposed to reduce the resource consumption of contention windows (CWs). Based on the proposed architecture, MCBC is modified to adapt to VANETs. The proposed CCH architecture supports parallel contention mechanisms and allows losers to change their target resource blocks (RBs) during the modified MCBC. Furthermore, to accelerate the RB assignment, a group contention strategy is developed by dividing the entire frame into several groups with different sizes. With the objective to maximize the ratio of the RBs allocated free of collisions within a single frame, a greedy group partition algorithm is proposed to determine the appropriate group sizes according to the MCBC capability. Evaluations are done to validate the proposed mechanism. Results show that OGCMAC achieves the highest throughput among all the evaluated MACs, because of its high channel usage rate. RBs are allocated faster by employing the proposed optimal group contention policy. It is also shown that the proposed greedy group partition algorithm can reduce the resource waste by properly adopting the existing CWs without extra resource consumption.

In this paper, we propose a novel Media Access Control (MAC) scheme for Cognitive Radio Ad Hoc Networks (CRAHNs). The scheme includes two mechanisms. One mechanism is called Exposed Terminal Secondary users Communication (ETSC), which allows a Secondary User (SU) to continue communication attempt even when the occupied channel could be used by a new Primary User (PU). The other is called Four-Stage RTS/CTS Back protocol (FSRCB) of PUs by which the communication links between PUs can be created after at most four RTS/CTS shacking attempts. FSRCB can support a PU receiver which is in the interference area of a SU sender, and decrease transmission power to increase frequency utilization efficiency. Compared with the traditional "PU-driven SU self- termination" scheme, our scheme can achieve much better performance, including lower dropping rate, higher success rate, higher throughput, higher channel utilization efficiency, and lower blocking probability.

This paper studies the joint transceiver design and power splitting (PS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. The objective of this work is to minimize transmit power by jointly optimizing the transmitter at a base station (BS), the PS factors and information decoding (ID) receivers at mobile stations (MSs) subject to both the mean-square-error (MSE) and energy harvesting (EH) constraints. To solve the formulated nonconvex optimization problem, a framework is proposed to iteratively solve a joint transmitter and PS factors optimization (JTxPS) sub-problem and a receiver side minimum mean-square error (MMSE) minimization subproblem. The nonconvex JTxPS sub-problem is reformulated as a convex semidefinite programming (SDP) and thus solved. Simulation results show the effectiveness of the proposed scheme.

Cyber-physical systems (CPS) are emerging research areas that involve multiple disciplines. Two critical components in CPS are networking technologies and security. Because of the multi-disciplinary nature, the networking and security of CPS expand beyond traditional computing domains and have to consider the impact of applied physical systems. Therefore, new innovations are required such as novel transmission technologies, networking protocols, architectures, and security solutions. As a result, a significant amount of research work is expected for new models, performance analysis as well as evaluation, prototypes, and testbeds. This special issue focuses on research interests and activities related to networking and security in SmartGrid, Transportation and Medical Systems, with an emphasis on original analytical, experimental, and systems-related papers in these target domains.

Cross-layer communication could bring a significant performance improvement for wireless communication systems through allowing information exchanges among multiple layers. For delay-sensitive video application, cross-layer optimization could be more critical by jointly considering the network resource allocation and application performance requirement, especially in an interference-limited distributed network. To improve the quality of video transmission over an ad hoc network, this paper proposes a cross-layer scheme to maximize the average received video quality by jointly considering the application performance and network transmission strategy. In this scheme, important system parameters including the distribution of the node and the resulting interference, link transmission policy, and queueing delay, as well as the video encoding rate are jointly considered to achieve the best received video quality. To this end, a threshold-based transmission strategy and an interference approximation model are developed first. Then, the queueing delay is analyzed based on the M/M/1 queue model. Finally, the problem of maximizing video transmission quality is formulated as a cross-layer optimization problem. To solve this optimization problem, a distributed algorithm is proposed based on the optimization and game theories. The convergence performance of this distributed algorithm is analyzed and shown with relatively lower complexity through simulation. Furthermore, both numerical and simulation results show that the proposed scheme improves the system performance considerably based on the comparison with other two existing schemes.

Robotic sensor networks provide an effective approach for underground pipeline inspection. Such networks are comprised of sensor nodes (SNs) and relay nodes (RNs) carried by robots for information sensing and communication, and are able to perform accurate and realtime inspection, especially in adverse environments. SN localization is critical in such networks because localization results can be used not only for locating and pinpointing leaks, but also for maneuvering mobile SNs in a pipeline of complex configuration. However, both the underground operational environment and the limited resources of the SNs pose significant challenges for SN localization. This paper presents algorithms for SN localization in robotic sensor networks for underground pipeline inspection. Specifically, self-localization of underground in-pipe SNs were investigated by taking into account SN movement dynamics, and using the measurements of the SN's velocity and the received signal strength (RSS) of the radio signal from aboveground RNs. Depending on the availability of the RSS at the SN, different localization algorithms based on the Kalman filter are proposed for different scenarios. Simulation results show the efficacy of the proposed algorithms. The framework also provides insight into the design of robotic sensor networks for the inspection and maintenance of other types of pipeline systems, such as oil and gas pipelines.

Abstract Interactive multimedia applications such as peer‐to‐peer (P2P) video services over the Internet have gained increasing popularity during the past few years. However, the adopted Internet‐based P2P overlay network architecture hides the underlying network topology, assuming that channel quality is always in perfect condition. Because of the time‐varying nature of wireless channels, this hardly meets the user‐perceived video quality requirement when used in wireless environments. Considering the tightly coupled relationship between P2P overlay networks and the underlying networks, we propose a distributed utility‐based scheduling algorithm on the basis of a quality‐driven cross‐layer design framework to jointly optimize the parameters of different network layers to achieve highly improved video quality for P2P video streaming services in wireless networks. In this paper, the quality‐driven P2P scheduling algorithm is formulated into a distributed utility‐based distortion‐delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Specifically, encoding behaviors, network congestion, Automatic Repeat Request/Query (ARQ), and modulation and coding are jointly considered. Then, we provide the algorithmic solution to the formulated problem. The distributed optimization running on each peer node adopted in the proposed scheduling algorithm greatly reduces the computational intensity. Extensive experimental results also demonstrate 4–14 dB quality enhancement in terms of peak signal‐to‐noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

Physical layer security approaches enable secure message transmission without upper layer data encryption and thus draw intensive attention recently years. Following this topic, this paper proposes a novel approach to improve the security of multiple-input single-output (MISO) communications links in the presence of non-colluding passive Poisson distributed eavesdroppers. In the proposed approach, it is assumed that the channel state information (CSI) of the main channel is known and that of the eavesdropper channel is unknown. Through beamforming vectors, the transmitter transmits information signal to the legitimate receiver along with artificial noise (AN) to confuse the eavesdroppers. Secrecy outage probability (SOP) is adopted to describe the secrecy performance, and based on it, security region (SR) is used from the perspective of space to illustrate the security. In obtaining the SOP of the described transmission link, stochastic geometry theory is adopted. It is shown that the stochastic geometry theory provides a powerful tool in obtaining a solution of SOP. Furthermore, the secrecy performance between transmitting approaches with AN and without AN is compared. The SR is plotted and the factors impacting security are analyzed accordingly.

The secrecy rate optimization of wireless communication systems with full-duplex (FD) relays and jamming signals is investigated in this work. Cooperated with FD relays, a novel secrecy transmission mechanism is proposed targeting at creating interference at eavesdroppers by adopting jamming signals. In the proposed mechanism, relays work in FD mode to receive information signals and forward them together with extra jamming signals. The global channel state information (CSI) is assumed available at all transmit nodes. Based on the proposed scheme, the secrecy rate of relay communication system is analyzed. Simulation results are also included to support the theoretical analysis. Results show that the proposed scheme can obviously enhance the secrecy rate of relay communication systems.

Sums of exponentially distributed random variables (RVs) play important roles in performance analysis of various communication systems. Their logarithmic expectations can not only facilitate capacity analysis but also provide efficient analytical expressions of the system capacity. However, the analytical expressions for the logarithmic expectations have been rarely systematically provided in literature, resulting in inconvenience for related performance analysis. To overcome this issue, in this work, the analytical expressions for the logarithmic expectations of the sums of independent exponential RVs are summarized. Especially, for the case where the sum is composed of both independent non-identically distributed (i.n.i.d.) exponential RVs and independent identically distributed (i.i.d.) exponential RVs, a new closed-form probability density function (PDF) is derived. Compared to the previous PDF expressions, the derived PDF expression is much concise and easy to be determined. To demonstrate the effectiveness of the derived logarithmic expectations, case studies are performed by applying the derived logarithmic expectations to the analysis and derivations of the ergodic capacity of several multiple antenna systems. It is shown that the proposed approach can significantly facilitate the performance evaluation of multiple antenna communication systems.

The articles in this special section are focused on two major aspects of Internet of things (IoT) technologies for smart and connected health services (SCH): 1) monitoring and assisting individuals by means of smart systems including sensors, devices, and robotics; and 2) creating interoperable digital health information infrastructures to increase medical/health information availability and use. The papers published in this SI provide evidence that SCH tools that rely upon IoT technologiescould significantly improve clinical outcomes and thequality of life of individuals undergoing monitoring.

Abstract Security and video quality are progressively significant attributes for wireless multimedia sensor networks. Most of existing research considers security and video quality separately. However, it is crucial to integrate security and video quality together for video transmission because delivering video data across a secure path does not often meet video quality requirements in many traditional approaches. Applying the general concept of secret sharing algorithm on a data packet and delivering it through disjoint multipaths can be considered to deliver the data securely. However, using the general concept of secret sharing is not efficient when large‐size video data are routed. To tackle these issues, we propose a novel security and quality aware routing (SQAR) protocol to address these two issues concurrently. We jointly consider security and video quality in wireless multimedia networks by proposing a video distortion model based on a new secret image sharing scheme. In SQAR, a secret image sharing is only applied on the intra‐frames of the video codec H.264 and can significantly reduce the transmission overheads. Simulation results show that SQAR scheme can achieve better trade‐off between the security and quality over the traditional routing protocols. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Coverage is an importance issue in wireless sensor networks. In this work, we first propose a novel notion of information coverage, which refers to the coverage efficiency of field information covered by deployed sensor nodes. On the basis of information coverage, we consider an optimization problem of how to partition the given field into multiple parcels and to deploy sensor nodes in some selected parcels such that the field information covered by the deployed sensor nodes meets the requirement. First, we develop two effective polynomial‐time algorithms to determine the deployed locations of source nodes for information 1‐coverage andq‐coverage of the field, respectively, without consideration of communication, where informationq‐coverage implies that the field information in terms of information point is covered by at leastqsource nodes. Also, we prove the upper bound in the theoretical for the approximate solution derived by our proposed method. Second, another polynomial‐time algorithm is presented for deriving the deployed locations of relay nodes. In the theoretical, this proposed algorithm can achieve the minimized number of relay nodes. Further, the related information 1‐coverage algorithms are applied in our wireless sensor network‐based automatic irrigation project in precision agriculture. Experimental results show the major trade‐offs of impact factors in sensor deployment and significant performance improvements achieved by our proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Numerous research works have been done on the Choquet integral model due to the tremendous usage in many fields. However, the application is still significantly restricted by the curse of dimensionality, involved in determining the non-additive interaction measures, that can properly reflect the interactions among predictive attributes toward the objective. To this end, in this paper we propose a novel determination method for non-additive interaction measures by the way of solving a sequence of least norm problems and iteratively updating the values of interaction measures, namely least norm learning. This method can achieve a significant reduction on the computation time complexity from O(m × 2n) to O(mn) for solving the Choquet integral model, where ra and n are the numbers of observations and attributes, respectively. Also we achieve to reduce the computation space complexity from O(m × 2n) to 0(2n). A case study on cross-layer optimized wireless multimedia communications is adopted to validate the proposed method. Both analytical and experimental results show the effectiveness of the proposed method.

Compressive sampling matching pursuit (CoSaMP) is an efficient compressive sensing algorithm holding rigorous estimation error bounds and low computational complexity, when it deals with an additive noise signal model in the observation domain. However, in some applications, e.g., speech enhancement (SE), noise is added to a signal in the time domain, where the conventional CoSaMP cannot be directly applied. In this paper, we establish the theory of CoSaMP to address the time-domain noise, referred to as Tdn-CoSaMP, which extends the canonical theory of CoSaMP. In particular, we prove the existence of a new upper bound of Tdn-CoSaMP, which is found to be larger than that of the conventional CoSaMP by appending two additional terms: a multiplier$1+\sqrt{{N\over s}}$, where$N$is the dimension of the signal, and an${\ell_1}$norm of the noise${1\over\sqrt{s}}\Vert {\mbi{e}}\Vert_1$scaled by the sparse level$s$of the signal. We also apply Tdn-CoSaMP to the SE task based on the sequential denoising of overlapped frames in the discrete cosine transform (DCT) domain. The proposed system, CoSaMP-based speech enhancement (CoSaMPSE), has been evaluated in terms of both objective and subjective criteria on various types of noise. Positive results have been achieved for denoising stationary and nonstationary white Gaussian noise (WGN) and are comparable to other SE methods. Moreover, due to its low computational complexity, CoSaMPSE is possible to be combined with optimally modified log-spectrum amplitude estimation (OMLSA) and able to achieve complementary denoising effects in various noisy conditions.

Wireless sensor networks (WSNs) provide an effective approach for underground pipeline inspection. Such WSNs comprise sensor nodes (SNs) and relay nodes (RNs) for information sensing and communication. WSNs can perform accurate and realtime inspection, especially in adverse environments. However, transmitting information between underground and aboveground nodes is very challenging. First, in-pipe SNs conducting controlled maneuvers underground are mobile. Second, SNs need to transmit the information wirelessly to aboveground base stations (BSs). In addition, radio propagation is complex because radio waves travel in a multi-medium environment. Finally, the SNs have limited energy supply. Therefore, proper deployment of a WSN is critical to providing reliable communications and efficient inspection. This paper presents a channel-aware methodology for deploying aboveground RNs in WSNs for underground pipeline inspection. Specifically, first, the paper provides a path loss model for radio propagation over multiple transmission media. Then, based on the path loss model a method is developed for optimum placement of the RNs so as to minimize the energy use of SNs and allow reliable communications. This method takes into account characteristics of the wireless channels, power consumption constraint, pipeline coverage requirement, and the limit of the number of the RNs. We provide an algorithm for optimization of RN placement and SN's power consumption. Simulation results show the efficacy of the proposed framework.

ABSTRACT Energy‐efficient routing becomes one of the most critical technologies for sustaining the overall network lifetime of wireless sensor networks. In this paper, we propose a novel data transmission scheme between a number of specified source nodes and the single sink, which can efficiently restrict the usage frequency of each relay node, measured by the number of source nodes using it for data transmission. On the basis of the importance of source nodes that is closely related to deployed location, they form a descending sequence such that each node finds the minimum energy path earlier than the succeeding one. Then, the energy‐efficient multiple path algorithm with the computational complexity ofO(n3) is developed for deriving the minimum energy paths, wherenis the number of nodes in the network. Also, a polynomial algorithm is presented for deriving the range of the feasible values ofN0serving as the threshold of the usage frequency of relay nodes, in which each can guarantee the existence of the solution. Further, we theoretically investigate the existence of the solution and the tree‐structured solution usingm‐ary tree. Extensive simulation results show that our proposed scheme can achieve significant performance enhancement. Copyright © 2012 John Wiley & Sons, Ltd.

In wireless sensor networks (WSNs), sensor nodes are usually powered by batteries of limited capacity, which results in dynamic changes of available paths for data aggregation due to node failures caused by energy depletion. For transmitting certain amount of data generated by source node, the overall transmission cost is affected by two major factors, using sequence of available paths and amount of data imposed on each path, which becomes a major issue significantly influencing the efficient usage of the networks. To address this issue, we consider the optimization problem of how to minimize the overall transmission cost of given data delivered from the source node to the sink node in the energy-constrained WSN. Specifically, we first describe the problem on the basis of the minimum cost flow theory and derive the upper bound for the data amount in terms of the number of packets that can be successfully transmitted from the source node to the sink node. Then, we propose specific algorithms to derive the optimal paths and their optimal data amounts, and then achieve the minimized overall transmission cost for the certain amount of data. Extensive simulations show that significant performance enhancement can be achieved by using our proposed algorithms.

In wireless sensor networks, one of the primary requirements is that sensor data derived from the physical world can be interchanged with all interested collaborative entities in a secure and reliable manner. Due to highly unpredictable environments where sensor nodes are usually deployed, minimizing the transmisstion cost with jointly taking into account the security of the whole network poses a challenging task. With this end, this paper considers an optimization problem of deriving the minimum cost paths from multiple source nodes, which are deployed in the area of interest, to the sink node under the constraint that the vulnerability of the whole network is under the given level. The vulnerability is defined as a metric which characterizes the degree of edge and node sharing among different paths. With the defined vulnerability, vulnerability-constrained minimum cost paths problem is formulated and two polynomial-time algorithms are developed for deriving the optimal paths. The necessary condition for the existence of the optimal solution, and the optimality of the proposed algorithms are analyzed in the theoretical. Extensive simulations show the significant performance enhancements achieved by our proposed algorithms.

In the authors previous work, a compressive sensing (CS)‐based method has been proposed to address speech enhancement (SE) in adverse environments (CS‐SPEN) based on an assumption of sparse noise. However, this assumption may not be satisfied in practical noisy environments. In this study, the authors study this issue by relaxing this assumption to consider a general non‐sparse noise case, such that the proposed method naturally extends the previous one. In particular, they solve the theoretic difficulty of CS‐SPEN on the treatment of non‐sparse noise by using a relaxed upper bound for the constraint governing data consistency and a relaxed estimation error bound. Their main result is mathematically proved. In addition, the effectiveness of the proposed method is demonstrated by computational simulations, showing certain improvements to the previous method for both stationary and non‐stationary white Gaussian noises across various segmental signal‐noise‐ratios (SNRs). In these cases, the proposed method is shown to have comparable results to the state‐of‐the‐art SE alogrithms and some advantages over them at low SNRs. CS‐SPEN without the sparse noise assumption works evenly with CS‐SPEN with the sparse noise assumption for car internal and F16 cockpit noises.

Rate control and adaptation is an important issue for video communications over cellular networks. In this paper, we propose a quality-aware feedback-free rate control and adaptation scheme for downlink real-time video communications over Long Term Evolution (LTE) networks. In our scheme, the send bit rate is adapted automatically according to the estimated packet loss due to the expiration of packet delay deadline based on queueing analysis by taking into account both packet queueing delay and transmission delay. This differs from existing rate adaptation methods which adapt send bit rate according to either packet loss or delay information explicitly feedback from the user side. The proposed scheme can maximize the system capacity in terms of the number of supportable users while providing satisfying user experience in terms of the received video quality and playback smoothness. Experimental results demonstrate the effectiveness of the proposed scheme.

Signal sparsity is the fundamental requirement of compressive sensing (CS) techniques. In our previous work, a CS-based speech enhancement algorithm has been proposed. However, several issues concerning speech sparsity have not yet been thoroughly studied. In this paper, we focus on studying the following issues: (1) the sparsity of clean speech and audio signals; (2) the sparsity of various noise signals; (3) analysis of the capacity of two sparse transforms i.e., wavelet and discrete cosine transform (DCT), to explore speech sparsity. In this respect, several measures are proposed to analytically compare the wavelet transform with DCT. We found that (1) signal compressibility is an important factor for the CS-based method. (2) DCT explores the best compressibility for noisy signals and achieves the best enhancement performance; (2) The CS-based speech enhancement methods are more efficient in reducing the noise with worse compressibility.

This paper introduced a camera surveillance system in wireless communications. The system contains three major modules, PTU (pan-tilt unit) camera control for surveillance video capture, cross-layer control for data compression and transmission, and error concealment for video quality enhancement. Our contribution is twofold. First, a system design for data collection and transmission over wireless networks is presented and is evaluated with physical surveillance equipments. The camera is capable of following the moving target according to the control information. The end-to-end distortion estimation in the delay constrained video coding process takes into account the dynamic channel condition and physical layer modulation and coding scheme (MCS) to determine optimal coding and transmission parameters. Second, multiple error concealment strategies, including interleaving, boundary match and video up-sampling, are applied utilizing the special property of the PTU camera motion.

Micro-electronic industry has been boosting the capability of wireless mobile devices on full-scale. However, battery, as the only power source of most mobile devices, is experiencing a relatively slow development. Therefore, how to optimally utilize the limited battery energy on mobile devices under a predefined performance requirement becomes a critical issue. On the other hand, it is still unclear that how the battery capacity consumption is allocated on different working pattern of a specific video codec under various tempo-spatial scales and parameters, which has posed a design challenge on power management on multimedia communication system. Furthermore, an optimization method is needed to be proposed and experimentally tested to achieve the tradeoff between the computational complexity and the distortion of multimedia delivery in order to discover the relationship and interaction between computational parameters of multimedia communication and battery capacity consumption. In this paper, we set up a measurement system to reveal the battery capacity consumption behavior and its footprinting in a video delivery system using H.264 codec. A systematic optimization framework which jointly considers the coding parameters and transmission parameters is proposed to achieve the tradeoff between battery capacity consumption and quality of services (QoS). Experimental results indicate insight of relationship between multimedia communication parameters and battery capacity consumption under a desired distortion requirement which provides a guideline of optimization for applicable multimedia system.

Large-margin estimation (LME) holds a property of good generalization on unseen test data. In our previous work, LME of HMMs has been successfully applied to some small-scale speech recognition tasks, using the SDP (semi-definite programming) technique. In this paper, we further extend the previous work by exploring a more efficient convex optimization method with the technique of second-order cone programming (SOCP). More specifically, we have studied and proposed several SOCP relaxation techniques to convert LME of HMMs in speech recognition into a standard SOCP problem so that LME can be solved with more efficient SOCP methods. The formulation is general enough to deal with various types of competing hypothesis space, such as N-best lists and word graphs. The proposed LME/SOCP approaches have been evaluated on two standard speech recognition tasks. The experimental results on the TIDIGITS task show that the SOCP method significantly outperforms the gradient descent method, and achieve comparable performance with SDP, but with 20-200 times faster speed, requiring less memory and computing resources. Furthermore, the proposed LME/SOCP method has also been successfully applied to a large vocabulary task using the Wall Street Journals (WSJ0) database. The WSJ-5k recognition results show that the proposed method yields better performance than the conventional approaches including maximum-likelihood estimation (MLE), maximum mutual information estimation (MMIE), and more recent boosted MMIE methods.

Cognitive radio (CR) has been proposed as a promising solution to improve connectivity, self-adaptability, and efficiency of spectrum usage. When used in video applications, user-perceived video quality experienced by secondary users is a very important performance metric to evaluate the effectiveness of CR technologies. However, most of the current research only considers spectrum utilization and effectiveness at medium access control (MAC) and physical layers, ignoring the system performance of the upper layers. Therefore, in this paper, we aim to improve the user experience of secondary users for wireless video services over CR networks. We propose a quality-driven cross-layer optimized system to maximize the expected user-perceived video quality at the receiver end under the constraint of packet delay bound. By formulating network functions such as encoder behavior, cognitive MAC scheduling, transmission, as well as modulation and coding into a distortion-delay optimization framework, important system parameters residing in different network layers are jointly optimized in a systematic way to achieve the best user-perceived video quality for secondary users in CR networks. Furthermore, the proposed problem is formulated into a MIN-MAX problem, and solved by using dynamic programming. The performance enhancement of the proposed system is evaluated through extensive experiments based on H.264/AVC.

The increasing popularity of ubiquitous, interconnected computing devices such as laptops, PDAs, and 3G mobile phones is fostering the emergence of environments where people access their personal information, corporate data and public resources "anytime" and "anywhere". Context, an important concept that can be any information regarding the situation and environment, is often exploited by human beings for communications and actions. Context-aware services have been proposed in the computing field to adapt system behaviors based on the retrieved context data. Although different context-aware systems have been presented in literature, this paper moves one step further by building an adaptive wireless multimedia system with context-awareness using ontology-based models. The proposed system can adapt its behaviors according to the changes of various context data. First, it chooses the appropriate video content based on the retrieved static context data such as user's profile, location, time, and weather forecast. Then, dynamic media adaption is performed to greatly improve the video quality perceived by the end user by adapting to various context data such as varying wireless channel quality, available energy of the end equipment, network congestion and application Quality of Services (QoS). To verify the effectiveness of proposed system, a test bench and its experimental results are also described.

Most existing works on routing for video transmission over multihop wireless networks only focus on how to satisfy the network-oriented quality-of-service (QoS), such as through put, delay, and packet loss rate rather than application-oriented QoS such as the user-perceived video quality. Although there are some research efforts which use application-centric video quality as the routing metric, they either calculate the video quality based on some predefined rate-distortion function or model without considering the impact of video coding and decoding (including error concealment) on routing, or use exhaustive search or heuristic methods to find the optimal path, leading to high computational complexity and/or suboptimal solutions. In this paper, we propose an application-centric routing framework for real-time video transmission in multihop wireless networks, where expected video distortion is adopted as the routing metric. The major contributions of this paper are: 1) the development of an efficient routing algorithm with the routing metric expressed in terms of the expected video distortion and being calculated on-the-fly, and 2) the development of a quality-driven cross-layer optimization framework to enhance the flexibility and robustness of routing by the joint optimization of routing path selection and video coding, thereby maximizing the user-perceived video quality under a given video playback delay constraint. Both theoretical and experimental results demonstrate that the proposed quality-driven application-centric routing approach can achieve a superior performance over existing network-centric routing approaches.

As a TCP-Friendly Rate Control protocol on the basis of TCP Reno's throughput equation, TFRC is designed to provide optimal service for unicast multimedia delivery over the wired Internet networks. However, when used in wireless environment, it suffers significant performance degradation. Most of the current research on this issue only focuses on the TFRC protocol itself, ignoring tightly-coupled relation between the transport layer and other network layers. In this paper, we propose a new approach to address this problem, integrating TFRC with application layer and physical layer to form a holistic design for real-time video streaming over wireless multi-hop mesh networks. The goal of the proposed approach is to achieve the best user-perceived video quality by jointly optimizing system parameters residing in different network layers, including the real-time video coding parameters at the application layer, the packet sending rate at the transport layer, and the modulation and coding scheme at the physical layer. The problem is formulated and solved as to find the optimal combination of parameters to minimize the end-to-end expected video distortion constrained by a given video playback delay. Experimental results have validated 2-4 dB PSNR gain achieved by the proposed approach in wireless multi-hop mesh networks.

Peer-to-Peer (P2P) video streaming services on the Internet have gained increasing popularity during the past few years. However, many problems still need to be addressed before it can be widely deployed in the wireless environment. The existing P2P overlay network architecture hides the underlying network topology, assuming channel quality is always in perfect condition. This works well for the Internet-based services, but hardly meets the user-perceived video quality requirement in wireless environments due to the time-varying nature of wireless channels. Inspired by the tightly-coupled relationship between P2P overlay networks and the underlying networks, we propose a novel scheduling algorithm on the basis of a quality-driven cross-layer design framework to jointly optimize the parameters of different network layers to achieve highly- improved video quality for P2P video streaming applications in multi-hop wireless mesh networks. In this paper, the quality-driven P2P scheduling algorithm is formulated into a distributed distortion-delay optimization problem, where the expected video distortion is minimized under the constraint of a given packet playback deadline to select the optimal combination of system parameters residing in different network layers. Then we provide the algorithmic solution to the formulated problem based on dynamic programming. The distributed optimization running on each partner node adopted in the scheduling algorithm greatly reduces the computational intensity. Extensive experimental results demonstrate 5-15dB quality enhancement in terms of PSNR by using the proposed scheduling algorithm.

Routing for video transmissions over multi-hop wireless networks has gained increasing research interest in recent years. However, most existing works only focus on how to satisfy the network-oriented QoS, such as, throughput, delay, and packet loss rate rather than the user perceived quality. Although there are some research efforts which use application-centric video quality as the routing metric, the calculation of video quality is based on some predefined rate-distortion function or model without exploring the impact of video coding and decoding (including error concealment) on network path selection and the resulting received video quality. Moreover, unlike network- centric routing metrics, such as, hop count, average delay or average success probability of packet transmission, video distortion cannot be calculated either additively or multiplicatively in a hop-by-hop fashion due to the dependency among packets introduced by error concealment. As a result, most existing works use either exhaustive search or heuristic methods to find the optimal path, which leads to high computational complexity or suboptimal solutions to the routing problem of video transmission. In this paper, we propose an application- centric routing framework for real-time video transmission over multi-hop wireless networks, where expected video distortion is used as the routing metric. The major contributions of this work are: 1) the development of an efficient routing algorithm with the routing metric in terms of the expected video distortion being calculated on-the-fly, and 2) the development of a quality-driven cross-layer optimization framework to enhance the flexibility and robustness of routing by the joint optimization of routing path selection and video coding, thereby maximizing the user perceived video quality under a given video playback delay constraint. Both theoretical and experimental results demonstrate that the proposed quality-driven application-centric routing approach can achieve a superior performance over existing network-centric routing approaches.

Alpha-integration and alpha-GMM have been recently proposed for integrated stochastic modeling. However, there has not been an approach to date for estimating model parameters for alpha-GMM in a statistical way, based on a set of training data. In this letter, parameter updating formulas are mathematically derived based on maximum likelihood criterion using an adapted expectation-maximization algorithm. With this method, model parameters for alpha-GMM are reestimated in an iterative way. The updating formulas were found to be simple and systematically compatible with the GMM equations. This advantage renders the alpha-GMM a superset of the GMM but with similar computational complexity. This method has been effectively applied to realistic speaker recognition applications.

Real-time video communication over wireless multihop networks has gained significant interest in the last few years. In this paper, we focus our attentions on the problem of source coding and link adaptation for packetized video streaming in wireless multihop networks when network nodes are media-aware. We consider a system where source coding is employed at the video encoder by selecting the encoding mode of each individual macro-block, while error control is exercised through application-layer retransmissions at each media-aware network node. For this system model, the contribution of each communication link on the end-to-end video distortion is considered separately in order to achieve globally optimal source coding and ARQ error control. To reach the globally optimal solution, we formulate the problem of joint source and distributed error control (JSDEC) and devise a low-complexity solution algorithm based on dynamic programming. Extensive experiments have been carried out on the basis of H.264/AVC codec to demonstrate the effectiveness of the proposed algorithm over the existing joint source and channel coding (JSCC) algorithm in terms of PSNR perceived at the decoder under time-varying multihop wireless links.

TCP Friendly Rate Control (TFRC) has been designed to provide smoother sending rate than TCP for multimedia applications. However, most existing work on TFRC is restricted within exploring the performance of TFRC itself in wired or wireless networks without considering the interaction between TFRC and other network layers. This paper proposes a quality- driven TFRC framework for real-time video streaming, where real-time video coding at the application layer and the packet sending rate at the transport layer are jointly optimized. The proposed framework is formulated to find the optimal video coding parameters and the sending rate to minimize the end- to-end expected video distortion under a given video playback delay constraint. The proposed framework has been implemented and tested by using both H.264/AVC codec and NS-2 simulator. Experimental results demonstrate that the proposed joint optimization framework can significantly improve the received video quality over the existing schemes, especially when delay bound is tight.

Video transport over multi-hop wireless networks has received significant research interests recently. The majority of the research efforts in this field have been conducted taking the approach of cross-layer optimization. However, video content and user perceived quality have been largely ignored in existing work. In this paper, we integrate video content analysis into video transport over wireless mesh networks (WMN). A content-aware quality-driven cross-layer optimization framework is proposed to achieve the best end-to-end user perceived video quality. In our framework, the extracted video regions of interest (ROI) are discriminatingly coded, transmitted and protected in video encoding, network routing and packet scheduling by different network layers. We aim at the optimization of key parameters of each layer while focusing on their interactions across the holistic network protocol stack. The proposed framework is evaluated by H.264/AVC codec and WMN simulations. Experimental results demonstrate that the proposed framework can effectively provide a good user perceived video quality, especially when the delay requirement is stringent.

In this paper we propose a cross-layer optimized framework which jointly considers the video coding and transmission in wireless mesh networks. In our framework, video coding parameters, network path selection, MAC layer frame size, and modulation and coding schemes at PHY layer are systematically optimized across the entire network protocol stack. To achieve the goal of real-time video communication with high-quality and/or stringent delay requirements, the proposed framework is formulated into a minimum distortion and/or minimum delay problem. The formulated problem is efficiently solved as a Lagrange dual problem, and the solution is tested by H.264/AVC. The system performance in terms of the perceived video quality is compared between the cross-layer optimized system and the current system. Experimental results indicate the effectiveness and efficiency of the proposed optimization framework.

In this paper, we propose a link adaptation scheme to adapt the packet size as a crucial system parameter for quality of service (QoS) provisioning in multi-rate wireless networks. The link adaptation scheme combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion. We then study the goodput performance of the proposed link adaptation scheme at the medium access control (MAC) layer. We show that there exists an optimal packet size to achieve the maximum goodput in transmitting messages over transport layer sessions. Based on this observation, we provide an efficient and effective algorithm to search the optimal packet size. Finally, we propose a link adaptation architecture, through which the proposed link adaptation scheme can adapt packet sizes to instantaneous channel conditions to fulfill committed QoS in higher layers. Numerical results show that the system adopting the proposed optimal packet sizes has superior goodput performance over the systems using fixed packet sizes.

Video summarization has gained increased popularity in the emerging multimedia communication applications, however, very limited work has been conducted to address the transmission problem of video summary frames. In this paper, we propose a cross-layer optimization framework for delivering video summaries over wireless networks. Within a rate-distortion theoretical framework, the source coding, allowable retransmission, and adaptive modulation and coding have been jointly optimized, which reflects the joint selection of parameters at physical, data link and application layers. The goal is to achieve the best video quality and content coverage of the received summary frames and to meet the delay constraint. The problem is solved using Lagirangian relaxation and dynamic programming. Experimental results indicate the effectiveness and efficiency of the proposed optimization framework, especially when the delay budget imposed by the upper layer applications is small, where more than 10% distortion gain can be achieved.

To enhance spectral efficiency and channel utilization in future wireless communication systems, adaptive modulation and coding (AMC) has been advocated at the physical layer. However, since the constellation size and coding rate are chosen based on the corresponding carrier-to-noise ratio (CNR) region, AMC can not guarantee to achieve the maximum spectral efficiency, especially when the number of available AMC modes are limited and the current CNR is low. In this paper, we present a scheme for spectral efficiency enhancement under fading channels. The proposed scheme combines AMC at the physical layer with hybrid automatic repeat request (HARQ) at the data link layer in a cross-layer fashion, where the upper layer HARQ provides an effective means to fine-tune the performance of the lower layer AMC. Both type-I and type-II HARQ are considered in our design. We have analyzed the system PER performance and the spectral efficiency of the proposed cross- layer design scheme. Numerical results show that our scheme can achieve maximum spectral efficiency while satisfying QoS requirements. The cross-layer design with AMC and type-II HARQ provides a large spectral efficiency improvement over the design with AMC and type-I HARQ.

Video summarization has gained increased popularity in the emerging multimedia and communications applications. However, very limited work has been conducted to address the transmission problem of video summary frames. In this paper, we propose a cross-layer optimization framework for delivering video summaries over wireless networks. Within a rate-distortion theoretical framework, the source coding, allowable retransmission, and adaptive modulation and coding have been jointly optimized, which reflects the joint selection of parameters in physical, data link and application layers. The goal here is to achieve the best video quality and content coverage from the received summary frames as well as meet the delay constraint. In this paper, this problem is formulated and solved by using Lagrangian relaxation and dynamic programming. Simulation results indicate the effectiveness and efficiency of the proposed optimization framework, especially when the delay budget imposed by the upper layer applications is small, where more than 12% distortion gain can be achieved

In this paper, we focus on combining hybrid automatic repeat request (HARQ) at the data link layer and adaptive modulation and coding (AMC) at the physical layer in a cross-layer design approach. First, a new AMC adaptation scheme has been proposed with the constraints of system parameters like maximum retransmission number and packet error rate (PER). Then we analyze the system PER performance and the spectral efficiency of the proposed cross-layer AMC adaptation scheme. Numerical results show that our scheme can achieve good spectral efficiency while satisfying the target delay and goodput performance.

In this paper, we present a cross-layer design which combines adaptive modulation and coding (AMC) at the physical layer with type-II hybrid automatic repeat request (HARQ) at the data link layer for QoS provisioning under fading channels. To maximize system spectral efficiency as well as satisfy QoS parameters such as data rate, delay and packet error rate (PER), we have proposed a new cross-layer scheme, where AMC transmits incremental redundancy for combining decoding with type-II HARQ. The proposed cross-layer scheme is designed to meet the target packet error rate within the pre-defined number of retransmissions. Since the number of retransmissions can be determined by the maximum delay bound imposed by upper layer applications, the proposed scheme will provide guaranteed QoS in terms of data rate, delay and packet error rate under fading channels. We have theoretically analyzed the system PER performance, the spectral efficiency and goodput of the proposed cross-layer design scheme. Since the application packet size is one of the most crucial factors to affect the performance of the proposed scheme, we also introduce an adaptive algorithm to derive the optimal information packet size under the current channel condition to achieve the maximum throughput. Numerical results show that the proposed scheme can achieve the maximum spectral efficiency while satisfying the target delay, packet loss rate and goodput imposed by the applications.

Based on different partitioning rules for MQAM constellations. Nonstandard MLC/MQAM schemes had been proposed to reduce the calculation of the individual capacity. In this paper, performance of the proposed MLC/MQAM schemes in well-defined OFDM systems are discussed with punctured convolutional codes as component codes. Compared with traditional MLC/MQAM and MQAM without MLC in OFDM systems, simulation results show that the nonstandard MLC/MQAM schemes can get better performance and lower complexity.