Unmanned aerial vehicles (UAVs) have emerged as effective platforms for mobile edge computing (MEC), offering flexible and efficient computational support to ground users (GUs). Many practical applications, such as deep neural network inference tasks, generate subtasks with complex dependencies, significantly complicating scheduling and offloading decisions. In this paper, we study the joint optimization of UAV deployment, UAV-GU associations, and dependent task offloading decisions within a multi-UAV-enabled MECsystem, aiming to minimize the end time of the overall tasks. The tasks generated by GUs are modeled using directed acyclic graphs (DAGs), explicitly capturing subtask dependencies and execution orders. To address the resulting complex optimization problem, we first propose a Joint Successive convex approximation and Penalty dual decomposition-based Optimization (JSPO) algorithm to determine the initial UAV deployment and UAV-GU associations. Next, we formulate the dependent task offloading decision process as a Markov decision process (MDP), which is solved by employing deep reinforcement learning (DRL). To effectively exploit the structural information within DAG tasks, we integrate a graph attention network (GAT) to provide enhanced state representations for DRL. JSPO and the DRL framework were executed in turns to gradually improve the performance. Extensive simulation results verify that our proposed framework significantly reduces the end time compared to existing methods, demonstrating its superiority in multi-UAV MEC systems.

This paper studies the unmanned aerial vehicle (UAV)-enabled aerial monitoring assisted by simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs), in which one UAV aims to monitor a number of moving targets, and one STAR-RIS is installed on a building for assisting the UAV to broadcast the monitored information to both indoor and outdoor users. Due to the randomness of target movements over time, the UAV needs to adaptively adjust its flight trajectory to track them. This thus results in highly dynamic channel conditions and uncertain UAV energy consumption, which accordingly make the efficient aerial monitoring a challenging task. To address these challenges, we propose a STAR-RIS-aided UAV-enabled aerial monitoring framework, which aims to maximize the long-term average throughput for all users, through joint optimization of transmit beamforming, UAV trajectory, and STAR-RIS configuration, while ensuring the monitoring requirements under strict energy constraints. The formulated problem is a multi-stage stochastic optimization problem, due to the randomness of various system parameters. To handle this problem, we apply the Lyapunov optimization technique and introduce a virtual energy queue to transform it into a series of single-slot optimization subproblems that are solvable online. For each subproblem, we develop efficient algorithms to obtain a near-optimal solution, in which a penalty dual decomposition (PDD) approach is used for the transmit beamforming and STAR-RIS configuration optimization, and a sequential parametric convex approximation (SPCA) method is used for UAV trajectory optimization. Extensive simulations demonstrate that the proposed framework significantly outperforms benchmark schemes, effectively maximizing the throughput and energy efficiency under dynamic operational conditions.

The rapid growth of mobile video traffic, particularly in remote or poorly connected areas, challenges the scalability of cloud-based video delivery. Leveraging the broad coverage of Low Earth Orbit (LEO) satellites, this paper proposes a cooperative edge caching framework for scalable video coding (SVC) videos. We develop an online optimization approach to minimize video access delay by jointly optimizing layer-wise video caching and bandwidth allocation across satellites, while accounting for collaboration and resource constraints. Unlike prior work that assumes known system states, our method operates in an online setting with unknown and time-varying user demands. A multi-agent online learning algorithm based on the multi-armed bandit (MAB) framework is proposed, where each satellite independently learns its caching policy. Lagrangian duality is further applied to allocate downlink bandwidth efficiently. Simulation results demonstrate that the proposed method significantly reduces access delay compared to existing baselines, highlighting the importance of satellite collaboration and adaptive learning in dynamic environments.

The integration of mobile edge computing (MEC) with semantic communication (SemCom) has emerged as a promising solution to address the growing demand for computing services. However, challenges remain in balancing task delay and energy consumption for unmanned aerial vehicles (UAVs) providing dynamic edge services, as the diversity of tasks complicates offloading and resource allocation. This paper investigates a multi-modal task offloading problem for Task-Oriented SemCom (TOSC)-based low-altitude MEC networks. We aim to minimize the weighted sum of ground devices’ average task delay and UAV’s energy consumption by jointly optimizing task offloading indicators, as well as the three-dimensional (3D) trajectory and computing resources of the UAV. To solve the formulated nonconvex optimization problem, which involves discrete and continuous variables, we propose an exact Penalty-based Alternative and soft Penalty-based Proximal Policy Optimization (PA-P3O) algorithm. Specifically, we develop an Exact Penalty (EP)-based algorithm to address the equilibrium-constrained task offloading problem with the discrete offloading indicators. Then, the coupling between the 3D trajectory and computing resources of the UAV is modeled as a Markov decision process (MDP), and a Soft Penalty (SP)-based deep reinforcement learning (DRL) algorithm is proposed to address the high-dimensional action space. Simulation results demonstrate that our proposed algorithm outperforms benchmark schemes and reveal an elevation-angle-distance tradeoff.

Industrial Internet of Things (IIoT) communication serves as the core for connecting industrial devices, systems, and platforms. In addition to real-time performance, reliability, and security, fairness in device access and data transmission has become increasingly important. This paper integrates Reconfigurable Intelligent Surfaces (RIS) with Non-Orthogonal Multiple Access (NOMA) technology in 6G communications, establishing a synergistic integration to jointly elevate spectral efficiency and fairness. Deploying Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS), which provides$\mathbf{3 6 0}$-degree coverage, in factory environments helps improve signal coverage and reduce bit error rates. To tackle challenges such as beamforming coupling, dynamic decoding order, reflection path optimization, and phase adjustment -with particular attention to fairness for low-rate devices - a fairness-oriented optimization model is proposed. This model focuses on reflective channel beamforming, quality of service (QoS), and STAR-RIS phase shift matrices optimization. Pursuing the maximization of the minimum achievable rate under QoS constraints for this intricate non-convex problem, a two-layer iterative method is employed. The outer layer handles adaptive SIC decoding order updates, nesting an inner layer that tackles the coupled optimization of the base station beamforming and STAR-RIS coefficients. Simulation verification reveals: 1) The proposed algorithm significantly improves system fairness. 2) The integration of STAR-RIS and NOMA provides higher gain in device communication. 3) Optimizing RIS phase shifts and beamforming effectively enhances communication rates. 4) The proposed approach exhibits superior performance relative to other schemes.

Recently, active reconfigurable intelligent surfaces (RIS) have attracted much attention due to its ability to amplify signals. The combination of active RIS and unmanned aerial vehicle (UAV) can improve the performance of communication systems. For the multi-objective optimization problem of active RIS assisted UAV communication systems, this work proposes a new low-complexity scheme to maximize system energy efficiency (EE). Unlike previous work, we focus on the impact of UAV hovering altitude on the system's EE and separately investigate the effects of the amplification coefficient and phase shift of active RIS on the system's EE. We also adopt non-orthogonal multiple access (NOMA) technology to improve spectral efficiency, further boosting the system EE. To address challenges such as non-convexity caused by UAV altitude updates and the coupling between active RIS's amplification coefficients and phase shifts, this work proposes a block coordinate descent (BCD) iterative optimization framework that decomposes the problem into three subproblems. By solving each subproblem, the optimal UAV altitude and the amplification coefficients and phase shifts of the active RIS are derived to maximize system EE. Numerical results show that: 1) Compared with other benchmark schemes, the proposed active RIS-NOMA scheme achieves significant improvements in system EE. 2) UAV demonstrates great communication potential in active RIS-NOMA systems, especially when direct links are blocked.

Semantic communication is a novel communication paradigm that demonstrates great potential in information transmission applications, particularly in challenging scenarios characterized by low signal-to-noise ratio (SNR) conditions. It extracts task-relevant semantic information and performs end-to-end optimization of source and channel coding. However, current mainstream architectures do not account for the importance of features in the subsequent reconstruction process. This shortcoming leads to the transmission of all features, resulting in inefficient bandwidth utilization. Furthermore, existing methods reconstruct all images equally, regardless of their differences in complexity, which is inefficient and wastes computational resources. To address these issues, we develop an efficient semantic communication architecture, termed ESC. Specifically, we design feature selection and reconstruction modules to filter out unimportant information, addressing the problem of transmission feature redundancy and improving bandwidth utilization efficiency. In addition, we develop a multi-branch decoder architecture and an adaptive inference strategy to accommodate the varying complexities of images, allowing samples with satisfactory reconstruction results to exit the decoder network early, thus reducing inference costs. By introducing feature selection, our architecture’s reconstruction quality surpasses that of 5G systems and mainstream semantic communication under the same bandwidth on the Kodak and CLIC2021 datasets. Our adaptive inference strategy achieves speed-ups of approximately 1.37 × and 1.43 × respectively, with only minimal degradation in image reconstruction quality.

Semantic communication is an emerging paradigm with significant potential for image transmission. However, resource-efficient architecture design and resource allocation in this field have not received adequate research attention. This paper proposes a resource-efficient multi-branch semantic communication architecture based on saliency detection, aimed at optimizing computational efficiency in image transmission. The architecture leverages models with varying capacities to process regions of images with different complexities. We further address the problem of multi-user uplink semantic communication and resource allocation, focusing on minimizing the total energy consumption for communication and computation. The optimization problem, subject to user demand, computation, delay, and transmission power constraints, is non-convex due to the coupling of variables, making it challenging to solve. To tackle this, we introduce a two-level decomposition approach. The lower-level problem, given a fixed compression rate, is solved using Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission power and computation frequency. The upper-level problem, which optimizes the compression rate, is reformulated as a monotone optimization problem for efficient solution finding. Numerical results demonstrate that the proposed architecture significantly reduces computational resource usage while maintaining image quality, and the resource allocation strategy effectively minimizes energy consumption, outperforming baseline schemes in terms of energy efficiency.

Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizing network topology, which is a complex task due to the large solution space and the need for fault tolerance. This paper presents the AlphaSat algorithm, a two-phase approach to improve latency and network robustness in LEO constellations. In the initialization phase, Monte Carlo Tree Search (MCTS) is used to generate an initial topology by selecting links from a vast search space. In the refinement phase, an edge-switching method is applied to enhance network resilience and performance. AlphaSat is evaluated on OneWeb, Starlink, and Telesat mega-constellations, demonstrating superior performance over existing algorithms. The results show significant reductions in latency ranging from 4.7% to 44.5% and improvements in network robustness, increasing by 3.3% to 28.3%. Furthermore, AlphaSat effectively balances network load and optimizes power consumption, offering a promising solution for efficient and resilient LEO satellite network design.

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes.

The Multi-Layer Satellite Remote Sensing (SRS) integrates data collection by Low Earth Orbit (LEO) satellites and data processing assistance from Medium Earth Orbit (MEO) satellites, thereby playing a crucial role in scientific exploration. However, effectively controlling access to LEO satellites for processing data, especially considering the frequent handovers caused by speed differences, presents a significant challenge to achieving high energy efficiency services. To address this challenge, we explore cooperative dynamic access control based on efficient communication mechanisms, with the aim of prioritizing processed data volume and meeting energy consumption requirements for satellites. Specifically, we formulate the access control issue as an optimization problem and integrate it into the framework of partially observable Markov decision process (POMDP), considering MEO satellites’ limited observation ability. By employing Multi-agent Deep Reinforcement Learning (MADRL), we propose a novel dynamic access control algorithm named DAC to solve our featured problem. Specifically, for improving performance, communication-efficient cooperation among MEOs is enhanced through modeling decision-relevant information of fellow MEO satellites and maximizing mutual information with their actual data to extract precise awareness and enable the generation of concise message. Finally, we conduct comprehensive experiments and an ablation study spanning the Starlink, OneWeb, and Telesat mega-constellations. The results demonstrate that DAC increases the average system data processing volume by at least 13.5%, while meeting energy consumption constraints and outperforming baseline algorithms.

The diversity of devices, systems, and applications imposes stringent requirements on the Industrial Internet of Things (IIoT) regarding agility, reliability, and delay sensitivity. Network function virtualization (NFV) can provide on-demand service and flexible resource management for the IIoT. Of course, a highefficiency and time-saving NFV placement solution is critical for IIoT. Information processing requests in an actual industrial scenario is generally continuous and dynamic. In addition, industrial information systems pay more attention to the real-time nature of the information. Considering the above challenges and the fact that most previous optimization-based solutions cannot cope with the characteristics of dynamic requests, we propose a deep reinforcement learning-based method to solve the virtual network function (VNF) placement problem, called DeepVNP, which automatically places the VNF according to the current physical network state, aiming to reduce the placement cost and improve the profit of the NFV system. In addition, the Information Age (AoI) is introduced to measure the real-time freshness of information, and it is naturally integrated with delay constraints. Through simulations, we evaluate the convergence and performance of DeepVNP. Numerical results show that, compared with several existing solutions, DeepVNP performs well in terms of request acceptance rate, resource utilization, total system cost, and average AoI.

Ground surveillance faces challenges like fixed positions and obstructions, leading to increased use of cellular-connected Unmanned Aerial Vehicles (UAVs) for improved data calibration. However, UAV integration into air-to-ground (A2G) surveillance networks encounters unpredictability in surveillance tasks and severe uplink interference, where sophisticated resource management is required. In this paper, we develop a multi-stage stochastic optimization framework for cellular-connected UAV surveillance with interference coordination. Our objective is to optimize the long-term average of the combined uplink throughput of both aerial and ground devices. This involves optimizing the power allocation and the UAV’s cell association across multiple orthogonal communication resource blocks (RBs). To address the inherent randomness and complexity of the formulated problem, we first transform the stochastic non-convex problem into a deterministic problem for each time slot using Lyapunov optimization framework. Subsequently, an online cell association and power allocation strategy is employed, by leveraging the optimal structure and the successive convex approximation (SCA) method. Simulation results demonstrate significant improvements in network throughput and UAV task queue stability, compared with existing baseline schemes.

Network Function Virtualization (NFV) supported Mobile Edge Cloud (MEC) is considered an ideal platform for low-latency Internet of Things (IoT) applications, where IoT application requests are represented as Service Function Chains (SFCs) which consists of a sequence of ordered Virtual Network Functions (VNFs). However, MEC’s limited resources can only support a limited number of IoT applications. In this scenario, how to effectively place SFCs to improve resource utilization and service quality under latency, resource constraints while considering the dynamic changes of network is a critical concern for infrastructure providers. In this paper, we study the SFC placement problem in NFV-enabled MEC and propose a Proximal Policy Optimization (PPO) based online SFC placement algorithm called SFCP-PPO. SFCP-PPO achieves the goal of maximizing long-term average revenue through the integration of two critical components: the Multi-Head Attention Mechanism (MHA), capable of extracting information from diverse network representation spaces, and the Recurrent Neural Network (RNN) that addresses scalability challenges posed by varying sizes of SFCs and reduces the frequency of acquiring physical network states during the SFC placement process. We demonstrate the effectiveness of SFCP-PPO through extensive experiments. Compared to existing benchmark algorithms, SFCP-PPO achieves an improvement of 8% in acceptance ratio and 6.5% in long-term average revenue with low running time.

Due to the characteristic of wide coverage, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have been employed to provide mobile crowdsensing and edge computing. However, the limited computation and onboard battery capacities of UAVs impose a changeling for timely computation and endurance. In this article, we consider an aerial edge computing system where multiple cellular-connected UAVs are employed to perform sensing and computation tasks over target subregions, and the UAVs can offload their computation tasks to the ground base station (BS) with the binary offloading scheme. We aim to minimize the maximum energy consumption of all UAVs by optimizing 3-D UAV trajectories jointly with the binary offloading indicator as well as computation resource allocation, subject to the target sensing constraints and the computation completion time constraints. The optimization problem we formulated is nonconvex and involves binary design variables, making it difficult to find the optimal solution. To address this challenge, we propose an efficient alternating optimization algorithm that can obtain a high-quality suboptimal solution, where the exact penalty method with equilibrium constraints is adopted to tackle the binary constraints. To tackle the nonconvexity of the optimization subproblems, we utilize the successive convex approximation approach to obtain a suboptimal solution. Extensive simulations are conducted and the results demonstrate that the proposed design significantly reduces the energy consumption of the UAVs over several baseline methods.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution for aerial surveillance applications, such as traffic monitoring, disaster management, and infrastructure inspection. However, in urban environments, ground-based obstructions frequently disrupt the Line-of-Sight (LoS) links between the unmanned aerial vehicle (UAV) and ground users (GUs), which leads to significant performance degradation in video streaming transmission. To enhance air-to-ground (A2G) communication quality, intelligent reflecting surfaces (IRSs) can be employed to construct reconfigurable UAV- IRS- GU links. In this article, we present a novel framework that integrates an IRS with a multiantenna UAV to enable high-quality aerial video streaming service for a group of GUs simultaneously. By jointly optimizing the UAV trajectory, operation time, transmit beamforming, and phase shifts, the total energy consumption of the UAV is minimized while satisfying Quality of Service (QoS) requirements for video streaming. The problem is formulated as an intractable nonconvex optimization problem with closely coupled variables. To tackle such a challenge, we propose a two-stage algorithm to obtain a suboptimal solution. The first stage focuses on minimizing the UAV’s propulsion energy consumption through path discretization, alternating optimization (AO), and successive convex approximation (SCA) techniques. The second stage focuses on communication energy minimization, achieved through a double-loop iterative algorithm based on the penalty-based block coordinate descent (P-BCD) technique. Simulation results verify the effectiveness of the proposed algorithms and show significant energy savings compared to several baseline schemes.

The incorporation of Unmanned Aerial Vehicles (UAVs) into cellular networks opens up new possibilities to enhance their ubiquitous operations and establish superior performance owing to the high probability of line-of-sight (LoS) for air-to-ground channels. However, this also results in the UAV inducing more significant uplink interference to non-associated Base Stations (BSs). This paper explores the online design policy in cellular-connected multiple UAV communications in the absence of channel conditions, focusing on wireless resource allocation and dynamic three-dimensional (3-D) path planning. Our objective is to maximize the minimum uplink throughput for all UAVs while considering the energy constraints of the UAVs. First, we implement an online design utilizing the achievable rate based on the estimated instantaneous channel state information (CSI) for the current time slot, and the expected data rate for future time slots based on channel distribution information (CDI). Our solution employs the exact penalty method along with alternating optimization and successive convex optimization methods. Second, we formulate an online design by merely using the achievable rate based on the estimated instantaneous CSI for the current time slot. We introduce an energy-triggered penalty term to regulate the energy consumption of the UAVs, resulting in a low-complexity solution even if the CDI is unavailable before the flight. Lastly, we conduct extensive simulations to corroborate our findings and provide comprehensive comparisons with other baseline schemes to underline the effectiveness of the proposed designs.

Unmanned aerial vehicles (UAVs) have a significant potential for sensing applications in further cellular networks due to their extensive coverage and flexible deployment. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV, which senses data with onboard sensors and uploads sensory data to the ground base stations (BSs). To evaluate the freshness of sensory data, we employ the concept of age of information (AoI), which is defined as the time elapsed since the latest successful transmission of sensory data. A lower AoI implies fresher sensory data, which may lead to the increase of UAV operation time. To balance such tradeoff, we aim to minimize the weighted sum of operation time and total AoI for the UAV by jointly optimizing transmission scheduling, BS association, as well as UAV trajectory. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve due to the time-varying propagation channels. To this end, we first characterize the average communication performance with statistic channel information, and then develop a search algorithm to obtain the optimal solution via employing the optimal structure as well as convex optimization techniques, while a low-complexity Double Graph based Algorithm (DGA) is developed to obtain a suboptimal solution. Then, by taking into account the site-specific performance and making fast decisions online, we propose a Deep reinforcement Learning Algorithm (DLA). Compared to DGA, DLA can adapt to the specific local environment and obtain a solution more rapidly once the training process is completed. Simulation results show that the proposed algorithms outperform the benchmarks about 30%, and achieve flexible tradeoff between operation time and AoI of UAV sensing, which is not available by considering just one objective.

Aerial video streaming is a promising application of unmanned aerial vehicles (UAVs), which extends video service from ground to three-dimensional (3D) airspaces. However, high data rates and smooth transmission are required along with ubiquitous and environment-aware communications. To this end, we study the quality of experience (QoE) maximization problem in this paper for aerial video streaming over 3D cellular networks in urban environments with building avoidance. Different from the typical channel model based optimization in prior works, we tackle the joint design of 3D UAV trajectory and transmission scheduling as well as playback rate adaption with an environment and channel knowledge map (ECKM) approach, which provides rich information about the location-specific channel for enabling environment-aware communications. Specifically, we first consider the scenario with perfect ECKM, and propose efficient algorithms to obtain suboptimal solutions by utilizing two graph models and the iterative parameter-enabled block coordinate descent method. For the scenario without such map information, we propose a dueling Deep Q-learning (DQL) solution with map construction such that the learning process can be facilitated for path planning. Simulation results are provided to demonstrate the improvement in QoE by the proposed solutions over baseline schemes, as well as a tradeoff between video quality and rate variation.

Non-Orthogonal Multiple Access (NOMA) allows for the sharing of communication link resources among multiple users, which increases spectrum efficiency. In this paper, we consider the NOMA-based mobile edge computing (MEC) networks with unmanned aerial vehicle (UAV), where convenient computation offloading services for ground devices (GDs) with NOMA is provided. The main focus is to investigate the computation capacity in terms of computation throughput, which is characterized by the total size of completed tasks achieved through air-ground collaboration. To guarantee the fairness of GDs, the minimum computation throughput for all GDs is maximized by jointly designing the UAV trajectory, channel relationship coefficient, and computation resource allocation, where the formulated problem is non-convex with closely coupled mixed-integer design variables. A novel penalty based iterative algorithm is proposed, where penalty term is employed to penalize non-integer solution and an inexact block coordinate decent method is adopted to avoid strong locality of the optimized solution, where the convergence is also proved. We conduct extensive simulations and show that our joint design algorithm outperforms other benchmark schemes.

In this paper, we consider an aerial video streaming scenario where multiple cellular connected UAVs are employed to capture videos from different Point of Interest (PoI) areas. The videos are transmitted to the base stations (BSs) such that ground users can share the visions of the UAVs. We aim to maximize the minimum quality of experience (QoE) of all users by optimizing transmission scheduling jointly with video playback rate and UAV trajectory design, where the uplink interference as well as the trade off between video quality and video smoothness are taken into account. The formulation problem is a mixed integer nonconvex optimization problem that is difficult to solve. We address it through an inexact block coordinate descent method with overlapped blocks of variables to improve optimization flexibly. To relax binary constraints, we adopt the exact penalty method with equilibrium constraints, where the exactness of the penalty function is guaranteed. In addition, successive convex approximation method is adopted to tackle the non-convexity of the optimization problem. Simulation results indicate that the proposed scheme achieves significant performance improvement compared with the baseline schemes, and reveal the tradeoff between video quality and playback smoothness.

Due to its high mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) have attracted wide attention in wireless communication in recent years. However, the delay requirements (e.g., video streaming, online game, etc.) may limit the UAV's mobility. In this paper, we consider a three-dimensional (3D) UAV communication network, where a UAV is employed to fly flexibly in 3D space to serve ground users with delay requirements. To characterize the fundamental tradeoff between throughput and delay, we introduce the minimum required rate for users and aim to maximize the minimum weighted sum of throughput and required rate for each user, via joint optimization of the 3D UAV trajectory as well as communication time and rate allocation. The formulated problem is a non-convex optimization problem, which is generally intractable. By decomposing the formulated problem into two subproblems, we propose an iterative algorithm by block coordinate descent and difference of two convex (D.C.) optimization as well as successive convex approximation (SCA) techniques. Finally, extensive simulation results show that our proposed solution outperforms baseline schemes and unveils the interesting insights and tradeoff between throughput and delay over 3D UAV communication networks.

Unmanned aerial vehicle (UAV) is a promising solution to flexibly provide video service for scenarios with temporary traffic. Compared with single-antenna UAV, multi-antenna UAV improves the spectrum efficiency for video transmission. In this paper, we investigate a multi-antenna UAV-enabled streaming system for providing video service to multiple ground users (GUs) simultaneously. To fully utilize the spatial multiplexing gain brought by multiple antennas, we aim to maximize the minimum quality of experience (QoE) for GUs through joint optimization of video playback rate and transmission scheduling as well as UAV trajectory, where the tradeoff between video quality and video playback fluctuation are also taken into account. The optimization problem is formulated as a challenging mixed-integer nonlinear optimization problem. A double-loop iterative algorithm is proposed to obtain a suboptimal solution by employing penalty block coordinate descent (P-BCD) technique. To reduce the influence induced by violation of equality constraint, we update the penalty parameter in the outer loop. In the inner loop, we solve the penalized problem with given penalty parameter through BCD and ConCave-Convex procedure (CCCP) as well as successive convex approximation (SCA) techniques. Simulation results illustrate remarkable performance gains of our proposed scheme compared to benchmarks, which also reveals the tradeoff between quality and playback fluctuation of video.

Utilizing unmanned aerial vehicles (UAVs) as edge caching devices to provide scalable video services to ground users is a promising endeavor, where the caching placement should balance the trade-off between video quality and video diversity. In this paper, we investigate the scalable video coding (SVC)-based layered caching scheme in cache-enabled multi-UAV networks, where specified layers of video are cached at different UAV s. We aim to minimize the aggregate video access delay of all users, via joint design of layered caching placement and UAV deployment as well as user association to provide video services with different qualities. A mixed-integer non-convex optimization problem is formulated which is arduous to solve directly, we decomposed the original problem into two subproblems and proposed corresponding algorithms, i.e., penalty successive convex approximation (P-SCA) based user association optimization algorithm and penalty difference-of-convex (P-DC) programming based UAV deployment and layered caching placement algorithm. An efficient iterative algorithm is proposed wherein we alternatively optimize two subproblems until the algorithm converges. Simulation results show that the proposed algorithm can achieve considerable benefits compared with other benchmark schemes in terms of video access delay.

Running intelligent applications on a satellite is in urgent need, which can help to extract useful information from massive surveillance or remote sensing data and return it to ground in time. However, the limited computing ability on a satellite prohibits it from completing the whole application by itself quickly. Within the circumstance of space-air-ground integrated network (SAGIN), we propose to offload part of the computation task from the satellite to the ground station with strong computing ability, through the introduction of airship, which can assist the satellite not only by relaying but also in computing. To save the energy consumption of the satellite and airship, task offloading policy and resource allocation, are investigated for a special task model supporting deep neural network (DNN), which is popular in intelligent application. An optimization problem is formulated, which is difficult to solve. We achieve the global optimal solution through the following operations: 1) Transform the formulated problem into two levels, with every level dealing with discrete or continuous variables exclusively; 2) Explore implicit monotonicity and convexity of concerned functions so as to solve the non-convex lower level problem optimally only with several rounds of bisection or Golden search methods; 3) Solve the upper level problem optimally by enumeration but with polynomial complexity. Numerical results verify the effectiveness of our proposed method.

Mobile edge computing (MEC) has been considered to provide computation services near the edge of mobile networks, while the unmanned aerial vehicle (UAV) is becoming an important integrated component to extend service coverage. In this paper, we consider a UAV-enabled MEC with binary computation offloading, where a UAV serves as an aerial edge server and each task of devices is either executing locally or offloading to the aerial edge server as a whole. To provide fairness among different ground devices, we aim to maximize the minimum computation throughput for all devices via the joint design of computing mode selection and UAV trajectory as well as resource allocation. The optimization problem is formulated as a mixed-integer nonlinear problem consisting of binary variables, which is difficult to tackle. The influence of non-binary solutions is penalized with a penalty function, based on which we develop an efficient iteration algorithm to obtain a suboptimal solution via leveraging the penalty successive convex approximation (P-SCA) method and difference of two convex (D.C.) optimization framework, where the algorithm is guaranteed to converge. Extensive simulations are conducted and the results with different system parameters show the effectiveness of the proposed joint design algorithm compared with other benchmark schemes.

Surveillance drones are unmanned aerial vehicles (UAVs) that are utilized to collect video recordings of targets. In this paper, we propose a novel design framework for aerial video surveillance in urban areas, where a cellular-connected UAV captures and transmits videos to the cellular network that services users. Fundamental challenges arise due to the limited onboard energy and quality of service (QoS) requirements over environment-dependent air-to-ground cellular links, where UAVs are usually served by the sidelobes of base stations (BSs). We aim to minimize the energy consumption of the UAV by jointly optimizing the mission completion time and UAV trajectory as well as transmission scheduling and association, subject to QoS constraints. The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem by taking into account building blockage and BS antenna patterns. We first consider the average performance for uncertain local environments, and obtain an efficient sub-optimal solution by employing graph theory and convex optimization techniques. Next, we investigate the site-specific performance for specific urban local environments. By reformulating the problem as a Markov decision process (MDP), a deep reinforcement learning (DRL) algorithm is proposed by employing a dueling deep Q-network (DQN) neural network model with only local observations of sampled rate measurements. Simulation results show that the proposed solutions achieve significant performance gains over baseline schemes.

Cellular-connected unmanned aerial vehicles (UAVs) are expected to become integral components of future cellular networks. To this end, one of the important problems to address is how to support energy-efficient UAV operation while maintaining reliable connectivity between those aerial users and cellular networks. In this paper, we aim to minimize the energy consumption of cellular-connected UAV via jointly designing the mission completion time and UAV trajectory, as well as communication base station (BS) associations, while ensuring a satisfactory communication connectivity with the ground cellular network during the UAV flight. An optimization problem is formulated by taking into account the UAV’s flight energy consumption and various practical aspects of the air-ground communication models, including BS antenna pattern, interference from non-associated BSs and local environment. The formulated problem is difficult to tackle due to the lack of closed-form expressions and non-convexity nature. To this end, we first assume that thechannel knowledge map(CKM) or radio map for the considered area is available, which contains rich information about the relatively stable (large-scale) channel parameters. By utilizingpath discretizationtechnique, we obtain a discretized equivalent problem and develop an efficient solution based on graph theory by employing convex optimization technique and a dynamic-weight shortest path algorithm over graph. Next, we study the more practical case that the CKM is unavailable initially. By transforming the optimization problem to a Markov decision process (MDP), we develop a deep reinforcement learning (DRL) algorithm based on multi-step learning and double Q-learning over a dueling Deep Q-Network (DQN) architecture, where the UAV acts as an agent to explore and learn its moving policy according to its local observations of the measured signal samples. Extensive simulations are carried out and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of energy-efficient UAV flight with connectivity requirements and unveil the tradeoff between UAV energy consumption and time duration along line segments.

Unmanned-aerial-vehicle (UAV)-enabled mobile-edge computing (MEC) has emerged as a promising paradigm to extend the coverage of computation service for Internet of Things (IoT) applications, which are usually time sensitive and computation intensive. In this article, a novel design framework is proposed for a multi-UAV-enabled MEC system, where edge servers are equipped on multiple UAVs to provide flexible computation assistance to IoT devices with hard deadlines. The aim is to maximize the number of served IoT devices through jointly optimizing UAV trajectory and service indicator as well as resource allocation and computation offloading, where the chosen IoT devices will complete their computation tasks on time under given energy budgets and co-channel interference is taken into account. We formulate the optimization problem as a mixed integer nonlinear programming (MINLP), which is challenging to solve directly. The problem is first reformulated to a more mathematically tractable form by adding a penalty term to the objective function. We then decouple the problem into two subproblems and develop an iterative algorithm by solving the two subproblems with alternating optimization and successive convex approximation techniques, where the proposed algorithm converges to a Karush–Kuhn–Tucker (KKT) solution. In addition, an efficient initialization scheme is proposed based on multiple traveling salesman problem with time windows (m-TSPTWs) method. Finally, simulation results are provided to demonstrate that the proposed joint design achieves significant performance gains over baseline schemes.

Unmanned aerial vehicles (UAVs) can be flexibly deployed to offload cellular traffic or to provide video services for emergency scenarios without infrastructure. However, the inherent resource allocation and three-dimensional (3D) aerial trajectory design have not been formally studied. In this paper, we study the joint resource allocation and 3D aerial trajectory design for dynamic adaptive streaming over HTTP (DASH)-enabled services in a UAV communication system, where a UAV is employed as a base station for multiuser video streaming. Various factors are taken into account, including video data rate, quality variation, communication outage, play interruption, etc. By adopting a video streaming utility model, two fundamental problems are formulated with different practical aims: the first problem maximizes the minimum utility for all users within a given time horizon such that max-min fairness can be provided, and the second problem minimizes the UAV operation time subject to the individual utility requirement for all users to prolong UAV endurance. To tackle the first non-convex problem, we decouple it into three sub-problems, and a three-stage iterative algorithm is proposed to obtain a suboptimal solution by solving the three sub-problems with successive convex approximation and alternating optimization techniques. An exponential search based algorithm is proposed for the second problem by utilizing the structure of the considered problem and a similar three-stage iterative algorithm. Extensive simulations are carried out to evaluate the performance, and the results show that our proposed designs significantly outperform baseline schemes. Furthermore, our results reveal new insights of UAV movement for video streaming and unveil the tradeoff between utility and quality variance.

Completion time and energy consumption of the unmanned aerial vehicle (UAV) are two important design aspects in UAV-enabled applications. In this article, we consider a UAV-enabled mobile-edge computing (MEC) system for Internet-of-Things (IoT) computation offloading with limited or no common cloud/edge infrastructure. We study the joint design of computation offloading and resource allocation, as well as UAV trajectory for minimization of energy consumption and completion time of the UAV, subject to the IoT devices' task and energy budget constraints. We first consider the UAV energy minimization problem without predetermined completion time, a discretized nonconvex equivalent problem is obtained by using the path discretization technique. An efficient alternating optimization algorithm for the discretized problem is proposed by decoupling it into two subproblems and addressing the two subproblems with successive convex approximation (SCA)-based algorithms iteratively. Subsequently, we focus on the completion time minimization problem, which is nonconvex and challenging to solve. By using the same path discretization approximation model to reformulate problem, a similar alternating optimization algorithm is proposed. Furthermore, we study the Pareto-optimal solution that balances the tradeoff between the UAV energy and completion time. The simulation results are provided to corroborate this article and show that the proposed designs outperform the baseline schemes. Our results unveil the tradeoff between completion time and energy consumption of the UAV for the MEC system, and the proposed solution can provide the performance close to the lower bound.

Unmanned aerial vehicle (UAV)-enabled communication has emerged as an appealing technology for efficient data collection in wireless sensor networks (WSNs). This paper considers a scenario where multiple UAVs collect data from a group of sensor nodes (SNs) on the ground. We study the fundamental tradeoff between the aerial cost, which is defined by the propulsion energy consumption and operation costs of all UAVs, and the ground cost, which is defined as the energy consumption of all SNs. To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the above two costs, by optimizing the UAV trajectory jointly with wake-up time allocation, as well as the transmit power of all SNs. As the formulated problem is non-convex, it is difficult to be optimally solved in general. To tackle this issue, we decouple it into two sub-problems: UAV trajectory and wake-up time allocation optimization, as well as SN transmit power optimization. We propose an iterative algorithm to solve the two sub-problems by leveraging successive convex approximation and alternating optimization techniques. In addition, a new approach is proposed to design the UAV initial trajectory with multiple travelling salesman problem (MTSP) technique. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for cost balance between UAVs and SNs.

Due to the coverage constraint of a wireless base station, mobile users suffer from the unstable network connection and poor service quality, especially for the prevalent video services. As an alternative solution, an unmanned aerial vehicle (UAV) is able to reach the cell edge and serve ground users (GUs). In this paper, we extend the UAV applications to the more challenging adaptive streaming service over fading channel. First, we decompose the system into different modules, and present mathematical models for each of them, including a trajectory model of the UAV, fading channels between the UAV and GUs, and video streaming utility. Second, we formulate the problem as a non-convex optimization problem by optimizing the UAV trajectory and transmit power allocation, jointly with transmission schedule and rate allocation for multiple users. The objective is to maximize the overall utility while guaranteeing the fairness among multiple users under the UAV energy budget and rate-outage probability constraints. Third, to tackle this problem, we first analyze the relationship between transmission rate and rate-outage probability over the fading channel, and then divide the original problem into three subproblems, which can be solved by leveraging the successive convex approximation technique. Furthermore, an overall iterative algorithm over the three subproblems is proposed to obtain a locally optimal solution by applying the block coordinate descent technique. Finally, through extensive experiments, we demonstrate that the proposed design can achieve almost 30% performance gain in terms of max-min streaming utility for all users, compared with other benchmark schemes.

Integrating unmanned aerial vehicles (UAVs) into cellular networks offers a promising solution to support their efficient operations and achieve high-quality communication with the ground. In this paper, we consider a cellular-connected UAV communication system, in which one energy-constrained UAV flies from a given initial location to a final location while uploading data to the ground base stations (GBSs) along its flight. We study the joint design of UAV operation time, communication scheduling, as well as UAV trajectory and transmit power to maximize the data uploading throughput, subject to the communication quality of service (QoS) requirement and UAV energy budget constraints. We first consider an offline design approach by utilizing only the channel distribution information (CDI) that is available prior to the UAV's flight, which is formulated as a non-convex optimization problem and challenging to solve. By using path disretization and successive convex approximation (SCA) techniques, an efficient alternating optimization algorithm is proposed, which can converge to a solution that satisfies the Karush-Kuhn-Tucker (KKT) conditions. Then, we further study the online design approach by utilizing the instantaneous channel state information (CSI) that is available to the UAV in real time along its flight. As the online design problem has similar structure as that of the offline design, an adaptive online optimization algorithm is proposed. To further reduce the computational complexity, a low-complexity online algorithm based on receding horizon optimization (RHO) is developed by utilizing a combined offline and online design approach. Simulations are conducted to corroborate our study and the results demonstrate the performance gain of proposed designs as compared to various baseline schemes. Furthermore, our results unveil the tradeoff between system throughput and UAV endurance in the considered system.

Unmanned aerial vehicle (UAV) enabled communication has emerged as an appealing technology in wireless sensor networks (WSNs) for efficient data collection. This paper studies the energy issues for data collection in UAV enabled WSNs. It is revealed that a fundamental tradeoff exists between the energy consumption of UAV and that of all sensor nodes (SNs). To characterize such a tradeoff, an optimization problem is formulated to minimize the weighted sum of the energy consumption of UAV and SNs, via jointly optimizing the UAV trajectory, mission completion time, as well as the wake-up scheduling for all SNs. As the formulated problem is a non-convex problem with infinite variables over time, it is difficult to be optimally solved. To tackle this issue, the original problem is transformed into a discretized equivalent with path discretization method, and then a locally optimal solution is obtained by applying the successive convex approximation and block coordinate descent techniques. Simulations are conducted to corroborate our study and show the flexible tradeoff achieved by the proposed design for energy balance between UAV and SNs.

To guarantee quality of experience (QoE) of video streaming for ground users with large obstacles that deteriorate the quality of links, unmanned aerial vehicle (UAV) is introduced in this paper as a relay to serve these users for HTTP adaptive streaming. By introducing the QoE utility model for users, we study the average QoE maximization problem in UAV-enabled relaying system by optimizing the UAV position along with the bandwidth and transmit power allocation for ground users, subject to the information causality constraint at the UAV relay. The optimization problem is formulated with a non-convex programming which is difficult to solve in general. By applying the successive convex approximation and block coordinate descent techniques, an efficient iterative algorithm is proposed to simultaneously update the UAV's position, transmit power and bandwidth allocation at each iteration, where the convergence is analyzed. Extensive simulations are conducted to show that the proposed solution can achieve significant gains for QoE in terms of the average streaming utility.

Energy consumption is one of the important design aspect for data collection in wireless sensor networks (WSNs). This paper studies data collection from a set of sensor nodes (SNs) in WSNs enabled by multiple unmanned aerial vehicles (UAVs). We aim to minimize the maximum mission completion time among all UAVs by jointly optimizing the UAV trajectory, as well as the wake-up scheduling and association for SNs, while ensuring that each SN can successfully upload the targeting amount of data with a given energy budget. The formulated problem is a non-convex problem which is difficult to be solved directly. To tackle this problem, we first propose a simple scheme that each UAV only collects data while hovering, termed as hovering mode (Hmode). For this mode, in order to find the optimized hovering locations for each SN and the serving order among all locations, we propose an efficient algorithm by leveraging the min-max multiple Traveling Salesman Problem (min-max m-TSP) and convex optimization techniques. Furthermore, we propose the more general scheme that enables continuous data collection even while flying, termed as flying mode (Fmode). By leveraging bisection method and time discretization technique, the original problem is transformed into a discretized equivalent with a finite number of optimization variables, based on which a Karush-Kuhn-Tucker (KKT) solution is obtained by applying the successive convex approximation (SCA) technique. The simulation results show that the proposed multi-UAV enabled data collection with joint trajectory and communication design achieves significant performance gains over the benchmark schemes.

To offload the traffic brought by the explosion of mobile video traffic on present cellular networks, unmanned aerial vehicle (UAV) with moving ability is introduced, where UAV can fly to the cell edge to serve ground users and help offload the video traffic. In this paper, we consider the joint optimization of trajectory design and transmission rate allocation for reliable video streaming in aerial vehicle networks, and formulate the problem with a non-convex programming. By using the bisection method over the relationship between transmission rate and outage probability, a sub-optimal solution is proposed by using success optimization methods, and the solution can be guaranteed to be a local optimal solution. Simulation results show that the proposed scheme can achieve significant gains in terms of transmission outage probability, which is an important performance metric for reliable transmission.

To address the explosively growing demand for mobile traffic, wireless device-to-device (D2D) networks have been introduced, where caching at user devices can be exploited to alleviate the burden on base stations. In this paper, we consider joint caching and transmission of scalable video coding (SVC) streaming over wireless D2D networks. We formulate a joint caching and transmission problem using integer linear programming to minimize the average download time for user, and prove that finding the optimal solution is NP-hard. A heuristic solution is proposed based on a two-phase sub-optimization problem focusing on caching and transmission decisions, which is solved by using relaxed linear programming. Simulation results show that the proposed scheme can significantly reduce the average download time in comparison with existing caching strategies.

In wireless coded cache network, data contents are cached in a number of mobile devices using an erasure correcting code, and a user retrieves content from other mobile devices using device-to-device communication. In this paper, we consider the repair problem when multiple devices that cache data contents fail or leave the network. By exploiting the wireless broadcast nature, we formulate the repair problem over the broadcast channels using an integer linear programming formulation, aiming at minimizing the number of necessary broadcast transmissions. We also study the construction of repair codes and propose a decentralized repair coding method. Simulation results show that the performance using our method outperforms the basic cooperative repair scheme for wired distributed storage systems.

To alleviate the pressure brought by the explosion of mobile video traffic on present cellular networks, small cell base stations (SBS) with caching ability are introduced. In this paper, we consider the caching strategy using network coding for mobile users over heterogeneous wireless network containing SBSs. We formulate an integer programming problem to minimize the average number of packets downloaded from SBSs under the constraint of cache capacity, and prove that finding the optimal coded caching placement for mobile users is NPHard. Heuristic solution is proposed which reveals the structural properties of cache allocation based on their popularity profiles and user mobility patterns. Simulation results demonstrate that our proposed caching strategies acquire significant performance gain compared with conventional caching policies.

Broadcasting data to multiple users is widely used in wireless applications. We consider a group of mobile users, within proximity of each other, who are interested in the same video content. Network coded broadcast and cooperative coded communication can improve transmission efficiency and throughput over wireless network separately. In this paper we consider the D2D-assisted wireless network coded broadcast problem for users with multiple interfaces to minimize the number of transmission slots. In order to obtain all needed packets, user can receive encoded packet according to cellular link and local cooperative D2D links simultaneously. We analyze the lower bound of number of transmission slots and formulate the problem with integer linear programming(ILP). We also develop heuristic solution for this setup, and simulation results show that our coding scheme significantly reduces the number of transmission slots.

According to data broadcast, we can satisfy multiple requests for the same data item in a broadcast tick. However, there is no significant breakthrough in performance improvement until recently that some studies proposed to use network coding in data broadcast. After broadcasting an encoded packet which encodes a number of data items, multiple clients can retrieve different requested data items in a broadcast tick. This not only utilizes bandwidth more efficiently, but also improves system performance. In this work, we propose a generalized encoding framework to incorporate network coding into data scheduling algorithms for on-demand broadcast. In the framework, data scheduling can be formulated as a weighted maximum clique problem in a graph where the weight of the clique is defined according to the performance objectives of the applications. Under the proposed framework, existing data scheduling algorithms for on-demand broadcast can be migrated into their corresponding coding versions while preserving their original criteria in scheduling data items. Our simulation results using a number of representative scheduling algorithms show that significant performance improvement can be achieved with coding.

This paper studies the recovery from multiple node failures in distributed storage systems. We design a mutually cooperative recovery (MCR) mechanism for multiple node failures. Via a cut-based analysis of the information flow graph, we obtain a lower bound of maintenance bandwidth based on MCR. For MCR, we also propose a transmission scheme and design a linear network coding scheme based on (η, κ) strong-MDS code, which is a generalization of (η, κ) MDS code. We prove that the maintenance bandwidth based on our transmission and coding schemes matches the lower bound, so the lower bound is tight and the transmission scheme and coding scheme for MCR are optimal. We also give numerical comparisons of MCR with other redundancy recovery mechanisms in storage cost and maintenance bandwidth to show the advantage of MCR.

Reliable multicast in wireless networks has been well studied in the sense to solve the feedback implosion issue, which, however, can not reduce the number of retransmissions in order to recover all lost packets at receivers. Most recently, it has been proposed to use network coding for reliable multicast in wireless LANs to reduce the number of retransmissions. In this paper, we propose two new models to further reduce the number of retransmissions for reliable multicast. In the first model, each retransmission encoding decision is made according to the latest ldquowantedrdquo packet set at all receivers. Thus, the maximum number of receivers can potentially decode out one ldquowantedrdquo packet from each encoded retransmission packet. Such a model is referred to as dynamic multicast retransmission encoding (DMRE) model. This model is a memoryless model where a receiver will not buffer encoded retransmission packets for later use. In the second model, a receiver will buffer all received encoded retransmission packets and decode out their ldquowantedrdquo packets at the end of the retransmission batch. Such a model is referred to as cache-based multicast retransmission encoding(CMRE) model. The problem to minimize the number of retransmissions under both DMRE and CMRE models are NP-hard. Effective heuristic algorithms are proposed in this paper. We analyze the impact of packet delivery ratio on the gain of network coding. We derive the lower bound of the expected number of retransmissions using network coding, which provides the insights of the maximum potential gain using network coding in reliable multicast.

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic user access patterns. Traditional on-demand broadcast is under the assumption that only one data item can be retrieved by mobile clients in each time unit. However, the above constraint limits bandwidth utilization and throughput of broadcast systems. In this paper, we consider data broadcast with network coding in real-time on-demand broadcast environments. We analyze the coding problem in on-demand broadcast and transform it into the problem of finding the maximum clique in graph theory. Based on our analysis, a novel algorithm called ADC is proposed. ADC considers both request overlapping and request timing requirement in request scheduling and fully exploits information about clients' cached and requested data items to implement a flexible coding mechanism. The advantages of our proposed algorithm over other traditional and coding assisted broadcast algorithms are shown through simulation results. Our algorithm not only reduces deadline miss ratio of requests, but also utilizes broadcast channel bandwidth efficiently.

This paper focuses on network coding based multirate multimedia streaming in directed networks and aims at maximizing the total layers received by all receivers, which directly determine the quality of video streaming. We consider the property of layered coding in video streaming and propose the layer separated network coding scheme (LSNC) for layered video streaming. Two algorithms OLSNC and SLSNC are proposed for LSNC based video streaming, where OLSNC achieves an optimal solution, while SLSNC is a polynomial time approximation algorithm. Simulation results show that LSNC is an efficient network coding scheme for multirate multimedia streaming, and the aggregated number of received layers of both OLSNC and SLSNC is very close to the theoretical upper bound in all configurations analyzed.

Recently, double binary circular turbo code has received tremendous attention. Due to its better error-correcting capability than classical turbo code, it has commenced practical applications in current communication standards, such as DVB-RSC and IEEE 802.16 (Wimax). However current decoding schemes will incur a huge computation complexity. In this paper, authors present a novel decoding scheme for double binary circular turbo codes, which will not only reduce the computation complexity, but also give at least 0.5 dB performance gain compared with current decoding schemes

A novel embedded dynamically reconfigurable fabric for implementing the Viterbi algorithm in a System-on-Chip device is presented in this paper. The proposed reconfigurable fabric can support Viterbi implementations for different standards, such as GSM, IS-95, CDMA and Wireless LAN. Our results illustrate that the proposed architecture has superior power consumption and throughput characteristics and it is demonstrated a 80% reduction in power consumption over generic field programmable gate array (FPGA) and 40 times improvement in throughput over digital signal processor (DSP), respectively. Thus, the reconfigurable system-on-chip platform based on this kind of domain specific reconfigurable fabrics is an efficient solution for the high-performance portable communication systems.

This work presents a novel embedded reconfigurable fabric targeting efficient implementation of the Viterbi decoder within a system-on-chip device. The proposed reconfigurable fabric can support constraint lengths ranging from 3 to 9, and code rates in the range 1/2-1/3.Our results demonstrate that this novel architecture has superior throughput and power consumption characteristics when compared to generic DSPs and FPGAs respectively.