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Multi-cell battery systems have been pervasively adopted as power supplies in industrial, commercial, and residential applications. Traditionally, battery systems consist of a large number of single cells interconnected by fixed topology to fulfill the requirements on voltage, current, capacity, and power. However, various cell unbalances introduced in manufacture and operational processes make battery systems suffer several major system-level deficiencies, including low energy efficiency, safety, second-use, and reliability issues. Therefore, dynamically reconfigurable battery (DRB) systems have gained popularity in recent years to achieve a higher energy efficiency and to prolong cycle life of battery systems by overcoming the adverse effects caused by cell unbalances. Unfortunately, current research on DRB systems mainly focuses on topology design with traditional management methods, lacking a theoretical foundation to study the performance optimization. In this paper, we propose a theoretical framework to optimize the DRB system performance by holistically considering various system design trade-offs on dynamical reconfiguration of the cell topology. Extensive numerical and experimental studies indicate the effectiveness and efficiency of the proposed optimization framework. Furthermore, the proposed framework can characterize the DRB system behaviors in a quantitative fashion, which provides design insights for such systems. A real-world case study has been provided to gain in-depth understanding of the proposed DRB systems.

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 $\epsilon $ -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.

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.

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.

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.

Wireless-Optical Broadband Access Network (WOBAN) is capacity-high, reliable, flexible, and ubiquitous, as it takes full advantage of the merits from both optical communication and wireless communication technologies. Similar to other access networks, the high energy consumption poses a great challenge for building up WOBANs. To shot this problem, we can make some load-light Optical Network Units (ONUs) sleep to reduce the energy consumption. Such operation, however, causes the increased packet delay. Jointly considering the energy consumption and transmission delay, we propose a delay-aware adaptive sleep mechanism. Specifically, we develop a new analytical method to evaluate the transmission delay and queuing delay over the optical part, instead of adopting M/M/1 queuing model. Meanwhile, we also analyze the access delay and queuing delay of the wireless part. Based on such developed delay models, we mathematically derive ONU’s optimal sleep time. In addition, we provide numerous simulation results to show the effectiveness of the proposed mechanism.

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.

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 4-14i?źdB quality enhancement in terms of peak signal-to-noise ratio by using the proposed scheduling algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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+√{N/s}, where N is the dimension of the signal, and an l1 norm of the noise [1/(√s)]||e||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.

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 and q-coverage of the field, respectively, without consideration of communication, where information q-coverage implies that the field information in terms of information point is covered by at least q source 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.

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.

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 of On3 is developed for deriving the minimum energy paths, where n is the number of nodes in the network. Also, a polynomial algorithm is presented for deriving the range of the feasible values of N0 serving 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 using m-ary tree. Extensive simulation results show that our proposed scheme can achieve significant performance enhancement. Copyright © 2012 John Wiley & Sons, Ltd.

Load sharing among distributed generation systems (DGS) is one of the most important topics in wireless-enabled smart microgrids, where a set of power inverters at different geographical locations work coordinately and collaboratively through wireless networks to meet the load requirement. Parallel power inverters operated in distributed generation systems rely on the underlying wireless communication channels to exchange power information among them for load sharing control. However, existing work in the area of load sharing has largely ignored the effect of wireless communication delay. Study has shown that the time-varying wireless communication delay may have a significant impact on the performance of power inverters. In this work, we aim to fill this gap and study the performance of distributed power inverters under the scenario of wireless-enabled smart microgrids. We first study the power inverter performance by taking into account the time-varying wireless communication delay. Then, we propose an optimal controller to overcome the delay problem and to provide a smooth load sharing performance. Both theoretical and simulation results show the effectiveness and responsiveness of the proposed controller design.

This article addresses cooperative video summary delivery over wireless networks which inherits advantages from both cooperative communication and video summarization. In this work, a novel decode-process-and-forward (DPF) scheme is proposed for video summary transmission, where a relay node with video processing capability is involved to generate a concise version of the summary frame, called summary of summary (SoS). The SoS information is effectively consumed by the destination side to enhance its error concealment capability, leading to an improved video reconstruction quality. This article proposes a generic crosslayer optimization framework for cooperative video summary transmission, which jointly considers the source coding, relay processing parameters, power allocation, and error concealment strategy to achieve the best video quality. The problem is solved by using Lagrangian relaxation and dynamic programming. Experimental results show that the proposed scheme significantly outperforms existing cooperative and non-cooperative transmission schemes.

Although there has been much work focused on the camera control issue on keeping tracking a target of interest, few has been done on jointly considering the video coding, video transmission, and camera control for effective and efficient video surveillance over wireless sensor and actuator networks (WSAN). In this work, we propose a framework for real-time video surveillance with pan-tilt cameras where the video coding and transmission as well as the automated camera control are jointly optimized by taking into account the surveillance video quality requirement and the resource constraint of WSANs. The main contributions of this work are: i) an automated camera control method is developed for moving target tracking based on the received surveillance video clip in consideration of the impact of video transmission delay on camera control decision making; ii) a content-aware video coding and transmission scheme is investigated to save network node resource and maximize the received video quality under the delay constraint of moving target monitoring. Both theoretical and experimental results demonstrate the superior performance of the proposed optimization framework over existing systems.

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.

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.

3GPP long term evolution is one of the major steps in mobile communication to enhance the user experience for next-generation mobile broadband networks. In LTE, orthogonal frequency-division multiple access is adopted in the downlink of its E-UTRA air interface. Although cross-layer techniques have been widely adopted in literature for dynamic resource allocation to maximize data rate in OFDMA wireless networks, application-oriented quality of service for video delivery, such as delay constraint and video distortion, have been largely ignored. However, for wireless video delivery in LTE, especially delay-bounded real-time video streaming, higher data rate could lead to higher packet loss rate, thus degrading the user-perceived video quality. In this article we present a new QoS-aware LTE OFDMA scheduling algorithm for wireless real-time video delivery over the downlink of LTE cellular networks to achieve the best user-perceived video quality under the given application delay constraint. In the proposed approach, system throughput, application QoS constraints, and scheduling fairness are jointly integrated into a cross-layer design framework to dynamically perform radio resource allocation for multiple users, and to effectively choose the optimal system parameters such as modulation and coding scheme and video encoding parameters to adapt to the varying channel quality of each resource block. Experimental results have shown significant performance enhancement of the proposed system.

Rate control is an important issue in video streaming applications. The most popular rate control scheme over wired networks is TCP-Friendly Rate Control (TFRC), which is designed to provide optimal transport service for unicast multimedia delivery based on the TCP Reno's throughput equation. It assumes perfect link quality, treating network congestion as the only reason for packet losses. Therefore, when used in wireless environment, it suffers significant performance degradation because of packet losses arising from time-varying link quality. Most current research focuses on enhancing the TFRC protocol itself, ignoring the 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 the application layer and the physical layer to form a holistic design for real-time video streaming over wireless multi-hop networks. The proposed approach can achieve the best user-perceived video quality by jointly optimizing system parameters residing in different network layers, including real-time video coding parameters at the application layer, packet sending rate at the transport layer, and 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, or to minimize the video playback delay constrained by a given end-to-end video distortion. Experimental results have validated 2-4dB PSNR performance gain of the proposed approach in wireless multi-hop networks by using H.264/AVC and NS-2.

Remote human motion tracking and gait analysis over wireless networks can be used for various e-healthcare systems for fast medical prognosis and diagnosis. However, most existing gait tracking systems rely on expensive equipment and take lengthy processes to collect gait data in a dedicated biomechanical environment, limiting their accessibility to small clinics located in remote areas. In this work we propose a new accurate and cost-effective e-healthcare system for fast human gait tracking over wireless networks, where gait data can be collected by using advanced video content analysis techniques with low-cost cameras in a general clinic environment. Furthermore, based on video content analysis, the extracted human motion region is coded, transmitted, and protected in video encoding with a higher priority against the insignificant background area to cope with limited communication bandwidth. In this way the encoder behavior and the modulation and coding scheme are jointly optimized in a holistic way to achieve the best user-perceived video quality over wireless networks. Experimental results using H.264/AVC demonstrate the validity and efficacy of the proposed system.

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.

Service-oriented architecture provides a solution to the increasing network complexity due to ever-growing heterogeneous networks. As the most significant component of SOA, the decision engine is to create a workflow, defined as a sequence of individual data processing entities, for providing end-to-end QoS of a given task. Although the workflow of video transmission is generally known, existing solutions are often monolithic. Furthermore, there is no decision engine to select a workflow based on the best user-perceived quality. In this article we propose a service-oriented decision engine framework, which consists of a decision engine, a performance evaluation component, and other major SOA components to support real-time video transmission over wireless multihop networks, aiming to provide the best user-perceived video quality under application-centric QoS constraints. Based on the investigation of the state of- the-art research efforts on SOA, some key issues for wireless live video transmission are discussed, and a case study for live video transmission is given to illustrate the proposed scheme. The superior performance of the proposed service-oriented decision engine is validated by experimental results.

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.

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.

With the fast-paced development of computing technologies, mobile devices have almost sufficient computation and communication capabilities to support mobile multimedia applications such as multimedia streaming, VoIP, and mobile TV. However, most existing mobile devices are powered by battery with limited energy resource. To support multimedia on battery-powered mobile devices, how to efficiently utilize the limited power source and other limited network resources has become one of the major challenges in mobile multimedia system design. It has been shown that achieving a satisfactory user experience needs a systematic consideration of both video source adaptation and network transmission adaptation, indicating that the core of mobile multimedia system design is how to achieve a good balance of the power consumption between computation usage and communication usage. In other words, it depends on how to jointly select video source parameters and channel parameters based on the video content characteristics, available network resources, and underlying network conditions, given the fact that the power management schemes as well as the nonlinear battery effects also affect the system power consumption of mobile devices. In this paper, we review the recent advances in power-aware mobile multimedia, especially the adaptation technologies applied in video coding and delivery. In addition, the major research challenges in the field are demonstrated and discussed, which include power-management for mobile devices, rate-distortion-complexity optimized video codec design, and computational complexity and power aware cross-layer design and optimization. At the end, we propose a number of future research directions for audiences to continue investigation in this field.