With the widespread deployment of WiFi devices, utilizing beamforming feedback for sensing has become a popular trend in WiFi systems. However, the singular value decomposition (SVD)-based feedback method adopted in existing WiFi standards performs poorly in sensing performance since it only aims at maximizing the communication performance. To address this, we propose an integrated sensing and communication (ISAC)-oriented beamforming feedback protocol, which provides different sensing information based on the sensing indicator. Accordingly, we develop different ISAC-oriented CSI compression methods for different sensing applications requiring different feedback information. Taking the angle of departure (AoD) as an example, an optimization problem is formulated to maximize the data rate while preserving the complete AoD information. To resolve it, we propose an iterative algorithm to obtain the suboptimal solution and a heuristic algorithm to reduce the computational complexity. We further extend the proposed compression method for the AoD information to the compression of the angle of arrival (AoA) and time of flight (ToF). Test results show that our proposal achieves both excellent communication and sensing performance and can be applied to various sensing applications, such as localization and action recognition.

Integrated sensing, communication, and computation (ISCC) has been regarded as a prospective technology for the next-generation wireless network, supporting human-centric intelligent applications. However, the delay sensitivity of these computation-intensive applications, especially in a multi-device ISCC system with limited resources, highlights the urgent need for efficient sensing task execution frameworks. To address this, we propose a resource-efficient sensing framework in this paper. Different from existing solutions, it features a novel action detection module deployed at each device to detect the onset of an action. Only time windows filled with signals of interest are offloaded to the edge server and processed by the edge recognition module, thus reducing overhead. Furthermore, we quantitatively analyze the sensing performance of the proposed sensing framework and formulate a sensing accuracy maximization problem under power, delay, and resource limitations for the multi-device ISCC system. By decomposing it into two subproblems, we develop an alternating direction method of multipliers (ADMM)-based distributed algorithm. It alternatively solves a sensing accuracy maximization subproblem at each device and employs a closed-form computation resource allocation strategy at the edge server till convergence. Finally, a real-world test is conducted using commodity wireless devices to validate the sensing performance analysis. Extensive test results demonstrate that our proposal achieves higher sensing accuracy under the limited resource compared to two baselines.

To cope with global climate change and monitor the concentration distribution of CO2 and CH4 in the troposphere, the greenhouse gases monitoring instrument (GMI) carried by the GF5-02 satellite was successfully launched on September 7, 2021. GMI utilizes spatial heterodyne spectroscopy techniques to acquire interferogram images. During the acquisition process, interferogram images are inevitably corrupted by various noises, such as defective pixels, baseline drifts and random noise, which will greatly degrade the quality of recovered spectra and further reduce the retrieval accuracy of greenhouse gases. In this article, we model the mixed noise removal of interferogram images as an inverse problem and propose a novel optimization framework by integrating the robust principal component analysis (RPCA), unidirectional total variation (UTV), and penalized least squares (PLS) together. Specifically, the low-rankness of interference fringes and sparsity of defective pixels are both considered in the RPCA model, and the directionality of interference fringes is enhanced by the UTV model, while the PLS model guarantees the fidelity and smoothness of the fitting baselines. An efficient alternating direction method of multipliers procedure is designed to solve the proposed framework. A number of experiments were conducted to illustrate the superior performance of the proposed framework in terms of visual quality and quantitative assessment, as compared with the state-of-the-art algorithms.

Integrated sensing, computation, and communication (ISCC) has been recently considered as a promising technique for beyond 5G systems. In ISCC systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module. In this module, a threshold is used for detecting whether the sensing target is static and thus the overhead can be reduced. Subsequently, we mathematically analyze the sensing performance of the proposed framework and theoretically prove its effectiveness with the help of the sampling theorem. Based on sensing performance models, we formulate a sensing performance maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy, in which the minimum resource is allocated to computation tasks, and the rest is devoted to the sensing task. Besides, a threshold selection policy is derived and the results further demonstrate the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis. Extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation mobile networks to embed sensing function on communication waveforms. A major challenge in ISAC is the effective integration of sensing and communication functions. Addressing this, this paper introduces a dual-functional waveform design that builds on the existing orthogonal frequency division multiplexing (OFDM) waveform. Unlike prior approaches that generally sacrifice communication performance to enhance sensing performance, our design contains a null-space sensing precoder that utilizes the null space of the communication channel to project additional sensing signals, thus improving the sensing functionality of the OFDM waveform without degrading any communication performance. We formulate a waveform optimization problem aimed at maximizing the sensing performance under the null-space sensing precoder and then propose a majorization-minimization (MM)-based waveform design algorithm. Additionally, to meet the real-time communication requirement in practice, we analyze the intrinsic characteristics of the high-performance sensing waveform and then develop a low-complexity waveform design algorithm. Simulation results show that the proposed MM-based algorithm can dramatically improve sensing performance without incurring any additional sensing power and degrading the communication performance. Furthermore, the low-complexity algorithm achieves substantial improvements in the sensing performance with much reduced computational complexity.

In integrated sensing, computation, and communication (ISCC) systems, the competition for communication and computation resources between sensing tasks for ambient intelligence and computation tasks from mobile devices becomes an increasingly challenging issue. To address it, we first propose an efficient sensing framework with a novel action detection module that can detect whether the sensing target is static. Subsequently, we analyze the sensing performance of the proposed framework and formulate a sensing accuracy maximization problem while guaranteeing the quality-of-service (QoS) requirements of tasks. To solve it, we propose an optimal resource allocation strategy and derive a threshold selection policy that demonstrates the necessity of the proposed sensing framework. Finally, a real-world test of action recognition tasks based on USRP B210 is conducted to verify the sensing performance analysis, and extensive experiments demonstrate the performance improvement of our proposal by comparing it with some benchmark schemes.

Online marketplace is a digital platform that connects buyers (demand) and sellers (supply) and provides exposure opportunities that individual participants would not otherwise have access to. Online marketplaces exist in a diverse set of domains and industries, for example, rideshare (Lyft, DiDi, Uber), house rental (Airbnb), real estate (Beke), online retail (Amazon, Ebay), job search (LinkedIn, Indeed.com, CareerBuilder), and food ordering and delivery (Doordash, Meituan). Besides academia, many companies and institutions are researching on topics specific to their particular domains. The fundamental mechanism of an online marketplace is to match supply and demand to generate transactions, with objectives considering service quality, participants experience, financial and operational efficiency. It is valuable to bring together researchers and practitioners from different application domains to discuss their experiences, challenges, and opportunities to leverage cross-domain knowledge. The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains. This workshop will follow a dual-track format. Track 1 covers the issues and algorithms pertinent to general online marketplaces as well as specific problems and applications arising from those diverse domains, such as ridesharing, online retail, food delivery, house rental, real estate, and more. Track 2 focuses on the state of the art advances in the computational jobs marketplace. Interesting challenges in this domain include the drastic increase of work from home or remote work, the imbalance between the demand and supply of the job market, the popularity of independent workers, the capability of helping job seekers on their whole job seeking journey and career development, the different objectives and behaviors of all major stakeholders in the ecosystem, e.g. job seekers, employers, recruiters and job agents.

The Greenhouse Gas Monitoring Instrument (GMI) is a short-wavelength infrared (SWIR) hyperspectral-resolution spectrometer onboard the Chinese satellite GaoFen-5 that uses a spatial heterodyne spectroscopy (SHS) interferometer to acquire interferograms. The GMI was designed to measure and study the source and sink processes of carbon dioxide and methane in the troposphere where the greenhouse effect occurs. In this study, the processing and geometric correction algorithms of the GMI Level 1 product (radiance spectrum) are introduced. The spectral quality and greenhouse gas (GHG) inversion ability of the Level 1 products are analyzed, and the results illustrate that the specifications meet the mission's requirements. An initial evaluation of the resolution, signal-to-noise ratio (SNR), and stability of the radiance spectrum reveals that the overall function and performance are within the design objectives. A comparison between our Level 1 products and the theoretical spectrum shows that the root mean square (rms) of the residual is approximately 0.8%, and the Level 1 products of the GMI captured within five months after observations have good spectral stability characteristics (less than 0.005 cm-1for Band 1, 0.003 cm-1for Band 2, 0.002 cm-1for Band 3, and 0.004 cm-1for Band 4). These results demonstrate that the GMI payload and the processing algorithm all work well and reliably. Furthermore, based on the Level 1 products, a GHG retrieval experiment is carried out, and the results are compared with data from Total Column Carbon Observing Network (TCCON) stations. The initial comparison of the XCO2results yields a value of 0.869 for R2(goodness of fit), 0.51 ppm for bias (mean of absolute error), and 0.53 ppm for σ (standard deviation of error). Similarly, the XCH4comparison yields values of 0.841 for R2, 4.64 ppb for bias, and 4.66 ppb for σ.

Abstract In wireless sensor networks, one of the primary requirements is that sensor data acquired from the physical world can be interchanged with all interested collaborative entities in a secure, reliable manner. Because of highly unpredictable nature of the environments caused by malicious attacks or potential threats, minimizing transmission cost between source and sink nodes with jointly considering the security of the whole network is a critical issue. This paper considers two optimization problems of deriving the minimum cost paths from multiple source nodes to the sink node under the guaranteed level of the vulnerability. The link or node vulnerability is defined as a metric, which characterizes the degree of link or node sharing among paths. With the defined link vulnerability, the link vulnerability‐constrained minimum cost paths problem is first formulated, and two polynomial‐time algorithms are developed for deriving the optimal paths. For the node‐vulnerability‐constrained minimum cost paths problem, we adopt the network conversion and then achieve the optimal solution with previous proposed algorithms. The necessary condition for solution existence, the optimality of the proposed algorithms, and the related properties of tree network are further theoretically analyzed. Extensive simulations show the significant performance improvements achieved by our proposed algorithms.Copyright © 2014 John Wiley & Sons, Ltd.

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.

Abstract Recently, benefiting from rapid development of energy harvesting technologies, the research trend of wireless sensor networks has shifted from the battery‐powered network to the one that can harvest energy from ambient environments. In such networks, a proper use of harvested energy poses plenty of challenges caused by numerous influence factors and complex application environments. Although numerous works have been based on the energy status of sensor nodes, no work refers to the issue of minimizing the overall data transmission cost by adjusting transmission power of nodes in energy‐harvesting wireless sensor networks. In this paper, we consider the optimization problem of deriving the energy‐neutral minimum cost paths between the source nodes and the sink node. By introducing the concept of energy‐neutral operation, we first propose a polynomial‐time optimal algorithm for finding the optimal path from a single source to the sink by adjusting the transmission powers. Based on the work earlier, another polynomial‐time algorithm is further proposed for finding the approximated optimal paths from multiple sources to the sink node. Also, we analyze the network capacity and present a near‐optimal algorithm based on the Ford–Fulkerson algorithm for approaching the maximum flow in the given network. We have validated our algorithms by various numerical results in terms of path capacity, least energy of nodes, energy ratio, and path cost. Simulation results show that the proposed algorithms achieve significant performance enhancements over existing schemes. Copyright © 2016 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.

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.

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.

In wireless sensor networks (WSNs), the nodes are usually powered by batteries with limited capacities, which has become one of the main challenges to the wide deployment of WSN-based applications. The overall network lifetime, which is usually determined by the node with the shortest lifetime because of the unbalanced energy consumption, becomes a major issue. Aiming at this, we consider the optimization problem of how to maximize the data amount that can be transmitted from the source node to the sink node, given an energy-limited wireless sensor network. Specifically, we first analyze the problem on the basis of the max-flow min-cut theorem to obtain the maximum relay capacity in terms of the number of successfully transmitted packets from the source node to the sink node, from the theoretical point of view. Then, we propose our algorithms to achieve the optimal solution. In this way, the expected network lifetime can be maximized. Further, we also prove the correctness of the proposed algorithms. Extensive simulations show that significant performance enhancement can be achieved by using our proposed algorithms.

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.

In the considered multiple access network, the primary users transmit in the allocated mutual orthogonal channels. Meanwhile, the secondary user conducts spectrum sensing and seeks transmission opportunities in the temporarily unoccupied primary channels. In order to improve the stable throughput of secondary user, we propose a cognitive relay strategy, which suggests the secondary user to intelligently relay packets for some selected primary users in addition to sending its own packets. The criterions of selecting primary users are given in system with and without sensing errors, respectively. Numerical results show the benefits of cognitive relay strategy in terms of secondary throughput.

In cognitive radio networks, multiple spectrum opportunities can be used together to satisfy the service requirement by spectrum aggregation. In this paper, an admission control algorithm and a spectrum assignment strategy are proposed in order for both increasing the spectrum aggregation aware access capacity and decreasing the channel switch times when the channel states change. Considering different bandwidth requirement of secondary users, the proposed greedy admission algorithm takes limited aggregation capability into account. The channel switch times of secondary users at sensing moments is minimized based on the prediction of primary activities and the corresponding channel state transitions. The concept of outage probability is introduced into the scheme to indicate the probability of channel switch. The numerical results show the performance improvement of the proposed algorithms.

In this paper, an optimal resource allocation scheme is proposed for multi-user multi-channel cognitive radio networks under imperfect spectrum sensing. The channel dynamic model and the sensing errors are considered together to derive the metric of mean delay for each user-channel combination based on the vacation queueing model. Finally, the optimal resource allocation is determined according to the average system delay by bipartite graph matching. The simulation results indicate that the proposed mean delay metric can represent the transmission performance successfully.

In this paper, based on the effective capacity theory, we identify the maximal arrival rate of secondary user that an arbitrary ON/OFF primary channel can sustain, in the presence of sensing errors. We find that, the arrival rate of secondary user with statistical QoS requirement, is limited by both the effective capacity provided by primary channel, and the packet collision probability constraint of primary user. In general, the above two constraints result in unequal arrival rates, exhibiting different impacts on spectrum utilization. Based on this observation, two spectrum utilization approaches, i.e., η-probability random access and sensing parameter adjustment, respectively, are proposed to fully utilize the transmission opportunities, depending on whether the sensing parameters could be adjusted or not. In specific, the η-probability random access approach reserves more transmission opportunities for other secondary users, which increases network throughput, while sensing parameter adjustment approach yields improved arrival rate for specific secondary user. Performances of the proposed approaches are validated by numerical results.

In this paper, the problem of the multi-user uplink scheduling in cognitive radio cellular network (CogCell) is investigated. The objective is to maximize the system throughput, while protecting the QoS of primary user (PU) from being affected by secondary user (SU). Here, PU's QoS is represented by its signal-to-interference-plus-noise (SINR) outage probability. It is equivalent to say that SU can increase its transmit power to enhance the system performance as long as PU's SINR outage probability does not exceed the predefined threshold. So the first scheduling algorithm is proposed to maximize the system throughput through utilizing the multi-user diversity. Different from the first algorithm which does not take the fairness among SUs into account, the second scheduling algorithm with considering proportional fairness among SUs is proposed. It is shown to provide a satisfactory tradeoff between maximizing the system throughput and achieving fairness among SUs. Finally, these proposed algorithms are validated through extensive simulations.

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.

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.

Cognitive radio (CR) has the potential to substantially improve the system capacity and adaptability of wireless mesh network (WMN). In this paper we investigate the achievable performance gain of cognitive wireless mesh network (CWMN), in which all nodes are equipped with CRs, by jointly optimizing spectrum allocation, routing and time scheduling. The formulated optimization problem aims to minimize the system activation time to satisfy the given traffic demands, under the constraint of multiple access interference and the limited available spectrum bands at each node. Then we develop a column generation (CG) approach to solve this problem. Our analytical model is validated by the simulation results, which provide a better performance compared with fixed bandwidth allocation.

In this paper, we study the problem of bit and power allocation in broadband cognitive radio system. In the broadband communication system, the primary transmitter employs Orthogonal Frequency Division Multiplexing (OFDM) technique on the whole bandwidth. A cognitive user, which has the ability of detecting the transmission of the primary link on each subcarrier, attempts to use the same bandwidth. The goal of this paper is to study how to optimally allocate bit and power on each subcarrier at the cognitive transmitter so that the sum-rate of the cognitive user is maximized under the condition that the primary transmission is not affected. Based on the framework presented by A. Jovicic and P. Viswanath, we first formulate the problem into an optimization problem with integer variables and then propose a greedy bit and power allocation algorithm. We also prove that the proposed algorithm is the optimal solution to the optimization problem.

IEEE 802.16e and 802.20 are emerging as two promising technologies for broadband wireless access systems. In order to improve capacity and coverage performance, both of them addressed a fractional frequency reuse (FFR) scheme to combat co-channel interference in multi-cell deployment, which is referred to as FFR16and FFR20respectively in this paper. As for the scheme of FFR20, virtual area partition is proposed in this paper, and fractional frequency reuse factor (FFRF) is achieved by tuning the parameter of signal strength ratio. The optimal combination of resource allocation strategy and signal strength ratio is determined through simulation. In order to compare the performance of FFR16and FFR20, three evaluation metrics are introduced, including average throughput, outage probability and spectrum efficiency. Simulation results show that outage probability is much lower under FFR20scheme, which goes beyond the acceptable range when FFRF is smaller than 2.4. The resource utilization efficiencies of both are continuously increasing with regard to FFRF within range [2.4, 3]. Besides, FFR20outperforms FFR16under given conditions, in terms of average throughput and spectrum efficiency.