Enhancing the quality of experience (QoE) in interactive video streaming (IVS) remains a persistent challenge due to the need for ultra-low latency and rising bandwidth demands. Conventional algorithms, whether rule-based or learning-based, are obsessed with achieving tight coupling between encoding and sending bitrate adaptations for low-latency guarantee. However, our measurement studies reveal alarming harms of tight coupling in suppressing throughput, encoding bitrates and smoothness, as application- and transport-layer bitrate adaptations inherently have different mechanisms and goals. To tackle this problem, we propose Octopus, the first loosely coupled cross-layer bitrate adaptation algorithm for IVS to maximize QoE. Instead of blind synchronization, Octopus promotes mutual cooperation and independence between encoding and sending bitrate adaptations by integrating a multi-head network with shortcut connections and auto-regressive action modules. Additionally, based on meta-imitation reinforcement learning, we design a network condition-aware online adaptation scheme that enables the loosely coupled policy to swiftly adapt to diverse and dynamic wireless networks. We implement Octopus on a testbed, a microcosm of real-world deployment, with transceiver pairs running WebRTC on the WeChat for Business dataset. Results show that Octopus outperforms state-of-the-art algorithms, either improving bitrates by 37.1%, or optimizing stalling rate and smoothness by 54.1% and 9.2%, or achieving all-around improvements.

Accurate bandwidth estimation (BWE) is critical for real-time communication (RTC) systems. Traditional heuristic approaches offer limited adaptability under dynamic networks, while online reinforcement learning (RL) suffers from high exploration costs and potential service disruptions. Offline RL, which leverages high-quality data collected from real-world environments, offers a promising alternative. However, challenges such as out-of-distribution (OOD) actions, policy extraction from behaviorally diverse datasets, and reliable deployment in production systems remain unsolved. We propose RBWE, a robust bandwidth estimation framework based on offline RL that integrates Q-ensemble (an ensemble of Q-functions) with a Gaussian mixture policy to mitigate OOD risks and enhance policy learning. A fallback mechanism ensures deployment stability by switching to heuristic methods under high uncertainty. Experimental results show that RBWE reduces overestimation errors by 18% and improves the 10th percentile Quality of Experience (QoE) by 18.6%, demonstrating its practical effectiveness in real-world RTC applications. The implementation is publicly available at https://github.com/jiu2021/RBWEoffline.

Integrated Sensing and Communication (ISAC) improves resource utilization efficiency by sharing resources between communication and radar sensing. With the development of ISAC technology, there is a growing focus on how to protect information at the physical layer to ensure communication security. In this context, the physical-layer security (PLS) of the near-field ISAC system is investigated, operating in nonorthogonal multiple access (NOMA) scenario. In our proposed system, the base station (BS) transmits confidential messages to multiple communication users (CUs) while wirelessly sensing a target. Consider a joint transmission beamforming design to support secure communication and ensure sensing requirements. Specifically, we derive the Cramér-Rao bound (CRB) for joint distance and angle sensing in the near-field, and maximise the secrecy rate for CUs under the CRB constraint imposed by target sensing. Since the beamforming problem is nonconvex and challenging to be solved, semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms are investigated to transform the objective problem into a convex one, providing local optimal solution after acceptable iterations. The beamforming and overall system performance are also evaluated and studied by means of analysis and simulation. Simulation results show that our proposed method leverages near-field joint angle and distance beamforming design, enhancing the performance of secure communication while meeting the sensing requirements in the ISAC system.

This paper reports a reform scheme for project-based courses of an engineering-practicing-oriented special class in our university, aiming at engaging undergraduate students in research and development via a project-based teaching/learning model. To address a number of key challenges in effective curriculum design, fair and comprehensive evaluation and stimulating students' innovative thinking for the current courses, we are working on formulating a two-year pipeline of progressive project-based courses, redesigning the course curriculum with novel contents, establishing a process-oriented and multi-dimensional course grading scheme, and implementing proposal-midterm-final defense stages.

With the rapid development and widespread application of VR/AR technology, maximizing the quality of immersive panoramic video services that match users' personal preferences and habits has become a long-standing challenge. Understanding the saliency region where users focus, based on data collected with HMDs (Head-mounted Displays), can promote multimedia encoding, transmission, and quality assessment. At the same time, large-scale datasets are essential for researchers and developers to explore short/long-term user behavior patterns and train AI models related to panoramic videos. However, existing panoramic video datasets often include low-frequency user head or eye movement data through short-term videos only, lacking sufficient data for analyzing users' Field of View (FoV) and generating video saliency regions.

Unmanned aerial vehicle (UAV) has received tremendous attention in the area of edge computing due to its flexible deployment and wide coverage accessibility. In weak infrastructure scenarios, multiple UAVs can form on-site edge computing clusters to handle the real-time tasks. Further, a multi-UAV enabled edge-cloud computing system is coined by cooperating the UAVs with remote cloud, which provides superior computing capability. However, the uneven distribution of tasks makes it difficult to meet the real-time requirements when load balancing is unavailable. To address above issue, a delay minimization problem for multi-UAV enabled edge-cloud cooperative offloading is investigated in this paper. The problem is formulated as a non-convex problem based on models that reflect characteristics of the system, such as ubiquitous network congestion, air-to-ground wireless channel and cooperative parallel computing. An efficient cooperative offloading algorithm is proposed to address the problem. Specifically, convex approximation is applied to make the original problem tractable, and Lyapunov optimization is utilized to make online task offloading decisions. Finally, the correctness of the models are verified through a practical UAV-edge computing platform. Simulations based on measurement results and real-world datasets indicate that, the proposed algorithm fully utilizes the available energy to significantly reduce the tasks' completion delay.

In this paper, we explore distributed edge computation offloading (DECO) that offloads computation to distributed edge devices connected wirelessly, which perform the offloaded computation in parallel. By integrating edge computing with parallel computing, DECO can substantially reduce the total computation delay. In particular, we study the fundamental problem of minimizing the total completion time of DECO. We show that the time-sharing based communication resource allocation always outperforms the bandwidth-sharing scheme, so that it suffices to focus on the time-sharing based communication scheduling. Based on the time-sharing scheme, we first establish some structural properties of the optimal communication scheduling policy. Then, given these properties, we develop an efficient algorithm that finds the optimal allocation of computation workloads. Next, based on the optimal computation allocation, we characterize the optimal scheduling order of communications, which exhibits an elegant structure: the optimal order is in the non-decreasing order of the ratio between a device’s computation rate and its communication time. Last, based on the optimal computation allocation and communication scheduling, we show that the optimal device selection problem is a submodular minimization problem, so that it can be solved efficiently using some existing methods. We further extend the study to the setting where devices are subject to maximum computation workload constraints, and develop an efficient algorithm that finds the optimal computation workload allocation. Our results provide useful insights for the optimal computation-communication co-design for DECO. We evaluate the theoretical findings using extensive simulations in both practical settings and controlled settings, which demonstrate the performance of DECO in practice and also the efficiency of our proposed schemes and algorithms for DECO.

Maximizing quality of experience (QoE) for interactive video streaming has been a long-standing challenge, as its delay-sensitive nature makes it more vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has demonstrated great potential, existing works are either limited by fixed models or require enormous data/time for online adaptation, which struggle to fit time-varying and diverse network states. Driven by these practical concerns, we perform large-scale measurements on WeChat for Business’s interactive video service to study real-world network fluctuations. Surprisingly, our analysis shows that, compared to time-varying network metrics, network sequences exhibit noticeable short-term continuity, sufficient for few-shot learning requirement. We thus propose Fiammetta, the first meta-RL-based bitrate adaptation algorithm for interactive video streaming. Building on the short-term continuity, Fiammetta accumulates learning experiences through offline meta-training and enables fast online adaptation to changing network states through few gradient updates. Moreover, Fiammetta innovatively incorporates a probing mechanism for real-time monitoring of network states, and proposes an adaptive meta-testing mechanism for seamless adaptation. We implement Fiammetta on a testbed whose end-to-end network follows the real-world WeChat for Business traces. The results show that Fiammetta outperforms prior algorithms significantly, improving video bitrate by 3.6%-16.2% without increasing stalling rate.

Distributed edge computation offloading makes use of distributed wireless edge devices to perform offloaded computation in parallel, which can substantially reduce the computation time. In this article, we explore distributed edge computation offloading where the computation workloads of edge devices are correlated with their communication workloads. In particular, we study the fundamental problem of computation workload allocation and communication scheduling for minimizing the total completion time of the computation offloading. To solve this problem, we need to tackle several challenges due to the precedence constraints of computations and communications, the interference constraints of wireless edge devices, and the correlation between computation and communication workloads. We consider preemptive, half-preemptive, and non-preemptive networks for the formulated problem, respectively. For each setting, we first develop a simplified problem of computation allocation, based on which we then devise an efficient and feasible policy that can arbitrarily approach the optimal policy. For half-preemptive and non-preemptive networks, we also characterize the optimal communication orders. Our results provides useful insights for the computation-communication co-design of distributed edge computation offloading. We evaluate the proposed algorithms using simulation results, which corroborate the advantages of the algorithms.

Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (ABR) algorithms are not optimized for the air-to-ground links, which usually fluctuate dramatically due to the dynamic flight states of the UAV. In this paper, we present SA-ABR, a new sensor-augmented system that generates ABR video streaming algorithms with the assistance of various kinds of inherent sensor data that are used to pilot UAVs. By incorporating the inherent sensor data with network observations, SA-ABR trains a deep reinforcement learning (DRL) model to extract salient features from the flight state information and automatically learn an ABR algorithm to adapt to the varying UAV channel capacity through the training process. SA-ABR does not rely on any assumptions or models about UAV's flight states or the environment, but instead, it makes decisions by exploiting temporal properties of past throughput through the long short-term memory (LSTM) to adapt itself to a wide range of highly dynamic environments. We have implemented SA-ABR in a commercial UAV and evaluated it in the wild. We compare SA-ABR with a variety of existing state-of-the-art ABR algorithms, and the results show that our system outperforms the best known existing ABR algorithm by 21.4% in terms of the average quality of experience (QoE) reward.

In scalable video coding (SVC) multicast, videos are encoded into several layers that represent multiple quality levels. Mobile users with different wireless channel conditions can obtain different numbers of layers and have different quality of experience (QoE). To enhance the QoE of the users that suffer from the worse channel quality, it is beneficial to stimulate users' cooperation in relaying enhancement layers. However, potential relays may be unwilling to truthfully cooperate with receivers, which results in the asymmetric information problem in relay selecting. In this paper, we model the video relaying selection as a market with multiple receivers (principals) and relays (agents), and solve the problem according to the contract theory. The proposed solution is divided into following two steps: first, contract design and item preselection, and second, matching between each principal and agent. We propose a contract parameter determination method termed as the Matching-Aware strategy. Different from traditional strategies, the proposed Matching-Aware strategy makes the contract competitive in principal-agent matching without knowing the probability distribution of relays' types. The matching step is undertaken by the base station with the purpose of maximizing the social welfare. Numerical results corroborate that the contract-based video relaying scheme can tackle the asymmetric information problem. Besides, compared with other two baseline strategies, the proposed Matching-Aware strategy achieves higher QoE.

An important issue in an edge computing (EC) network is to increase the utilities of the end users concurrently accessing the computation resources. In this paper, we consider the task offloading in EC-enabled networks where the end users efficiently utilize the dispersed computation and communication resources in a multi-path multi-hop manner. We propose a binary optimization framework that generalizes multi-hop wireless EC task offloading as jointly making decisions of server selecting and traffic routing in networks of arbitrary topology (JoSRAT). We further develop an approximation algorithm JoSRAT that enables for a fully distributed implementation together with the worst-case performance guarantees. Interestingly, our proposed distributed algorithm achieves nearly optimal in the numerical evaluations, significantly outperforming the worst-case guarantees. The proposed algorithm also outperforms a widely-used heuristic, i.e., First Fit, in terms of computational time complexity, indicating the superior capability of the proposed framework.

Connected devices in Internet-of-Things (IoT) continuously generate enormous amount of data, which is transient and would be requested by IoT application users, such as autonomous vehicles. Transmitting IoT data through wireless networks would lead to congestions and long delays, which can be tackled by caching IoT data at the network edge. However, it is challenging to jointly consider IoT data-transiency and dynamic context characteristics. In this paper, we advocate the use of deep reinforcement learning (DRL) to solve the problem of caching IoT data at the edge without knowing future IoT data popularity, user request pattern, and other context characteristics. By defining data freshness metrics, the aim of determining IoT data caching policy is to strike a balance between the communication cost and the loss of data freshness. Extensive simulation results corroborate that the proposed DRL-based IoT data caching policy outperforms other baseline policies.

With the growing popularization of Electric Vehicles (EVs), charging management has become an increasingly important research problem in smart cities. Different from plug-in charging technology, we alternatively enable the battery switch technology to provide fast EV charging (reduce the service waiting time from tens of minutes to a few minutes), by facilitating the switchable (fully-recharged) batteries maintained at CSs and also the batteries cycling procure to refresh their availability. Nevertheless, potential hot spot may still happen at CSs, due to running out of switchable batteries as well as long batteries charging queue. With this concern, we next propose a reservation based EV charging management scheme to alleviate such situation, considering EVs' anticipated charging reservations (including arrival time, expected charging time) to coordinate EVs' charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our enabling technology, in terms of minimized waiting time for the battery switch as the benefit of EV drivers, and higher number of batteries switched as the benefit of CSs.

In this paper, a non-orthogonal multiple access (NOMA)-enhanced scalable video coding (SVC) multicast scheme for cellular networks is proposed. This scheme combines the successive video-layer decoding in SVC with the successive interference cancellation (SIC) in NOMA, which enables a further reduction of the bottleneck effect imposed by cell-edge user equipments (UEs). Aiming at maximizing the overall video quality experienced by UEs in multiple multicast groups, the resource allocation for multiple groups and the scalable multicast scheduling within each group are formulated as a joint mixed-integer nonlinear programming problem. The formulated optimization problem is decoupled into a multi-group resource allocation (MRA) problem and multiple independent intra-group scalable multicast scheduling (IGSMS) subproblems. To solve IGSMS subproblems, we propose an optimal recursive algorithm, for which the optimal transmit power for each layer of the superposition coding needed in each iteration is derived in a closed form. The MRA problem is optimally solved via a knapsack approach. Extensive numerical results demonstrate the improved performance of the proposed NOMA-enhanced SVC multicast scheme over several baseline schemes.

Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance, automatic behavior analysis and event recognition, which integrate image processing, computer vision, and networking capabilities. In conventional multimedia IoT systems, videos captured by surveillance cameras are required to be delivered to remote IoT servers for video analysis. However, the long-distance transmission of a large volume of video chunks may cause congestions and delays due to limited network bandwidth. Nowadays, mobile devices, e.g., smart phones and tablets, are resource-abundant in computation and communication capabilities. Thus, these devices have the potential to extract features from videos for the remote IoT servers. By sending back only a few video features to the remote servers, the bandwidth starvation of delivering original video chunks can be avoided. In this paper, we propose an edge computing framework to enable cooperative processing on resource-abundant mobile devices for delay-sensitive multimedia IoT tasks. We identify that the key challenges in the proposed edge computing framework are to optimally form mobile devices into video processing groups and to dispatch video chunks to proper video processing groups. Based on the derived optimal matching theorem, we put forward a cooperative video processing scheme formed by two efficient algorithms to tackle above challenges, which achieves suboptimal performance on the human detection accuracy. The proposed scheme has been evaluated under diverse parameter settings. Extensive simulation confirms the superiority of the proposed scheme over other two baseline schemes.

Recently, many applications (e.g., wearable cognitive assistance) with the devices of Internet of Things (IoT) (e.g., Apple Watch and Google Glass) have been fast developed. However, the current Internet may not be suitable for the future IoT applications due to the limited capabilities of the data caching and content processing services with the existing Internet architecture. In this paper, we extend the named data networking and develop the object-oriented network (OON) as a novel Internet architecture to implement both the native data caching and content processing in the network layer. The datagrams with processable payloads as well as the cached contents are both referred to as the operable objects in OON for abstraction. With the proposed OON architecture, operable objects can be processed and transmitted by forwarding them to the subroutines of content processing programs and the interfaces of content deliveries, respectively, according to the proposed naming rules. For performance evaluation, we implement the dynamic adaptive multimedia streaming application atop the proposed OON architecture in ns-3. Our simulation results show that the proposed OON architecture can effectively increase the potential quality of experience for mobile users.

The traffic of the base station can be offloaded by content sharing through device-to-device (D2D) communications if popular on-demand contents have been cached in user devices. In D2D content sharing, the receiving user gains benefit by obtaining contents while the transmitting user has to consume the transmission energy. However, users are selfish and have no obligation to help others. To motivate user involvement in D2D content sharing, we propose an energy-aware incentive mechanism where the key idea is that physically neighboring users can form a collaborative group. In a collaborative group, a user obtains contents from other users while consuming energy on providing contents to other users. We model the problem as a coalition formation game with non- transferable utility. To solve the problem, we also propose an algorithm which is proved to be of convergence and stability. Finally, simulation results show that our proposed mechanism has significant performance gains compared with two baseline schemes.

Machine-to-machine (M2M) systems enable machines or devices to collect data, exchange information, and act on the environment without direct human intervention. A device in an M2M system not only collects data for its own usage but also shares the data with other devices automatically to achieve certain goals. Therefore, emerging M2M systems for civil transportation, electric power grid, medical treatment, industrial automation, etc., can be set up based on the networking of devices. In this survey paper, we first introduce the general architecture and communication networks for M2M systems. Then, we categorize emerging M2M systems according to the types of M2M context, M2M task, and M2M objective. We further survey recent solutions for M2M systems from both academia and industry with the insights of the M2M system categorization. Finally, we summarize challenges in developing M2M system solutions.

The transmission of simultaneous and latency-sensitive data puts forth a significant challenge for the smart grid communications. In this paper, we investigate the application of device-to-device (D2D) communications for the energy management in the electric distribution network. Specifically, we develop a D2D-assisted relaying framework to exploit the spatial diversity and the differentiated data rate requirements, which improves the spectral efficiency, especially for the scenarios that there are faults in the electric distribution network. We study the data transmission scheduling problem under the proposed D2D-assisted relaying framework, aiming to minimize the overall information loss rate, while taking into account the uncertainties in the communication latency. To this end, we first cast the data transmission scheduling problem as a two-stage stochastic programming problem and derive the solution. Then, we develop a real-time distributed data transmission scheduling scheme based on the sample path realizations. Extensive simulation results show significant performance improvement by using the proposed D2D-assisted relaying framework compared with two baseline frameworks for a variety of different cases.

To meet the explosive demand on delivering high-definition video steams over cellular networks, we design a Social-aware video multiCast (SoCast) system leveraging device-to-device (D2D) communications. One salient feature of SoCast is to stimulate effective cooperation among mobile users (clients), by making use of two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients form groups to obtain missing packets from other clients and restore incomplete video frames, according to the unique video encoding structure. In return, the user perception of the mobile video quality can be substantially improved. Specifically, we first cast the problem of social ties based group formation among clients for cooperative video multicast as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation scheme is proposed for the base station to handle D2D radio resource requests from client groups. Extensive numerical studies using real video traces corroborate the significant gain using SoCast.

Device-to-device (D2D) communication is known as a promising way to cope with the growing mobile video traffic, which may suffer from the short duration caused by the limited energy supply. In this paper, we propose a practical D2D cooperation framework based on distributed optimization to extend the video transmission duration. In the multi-path multi-hop D2D communication scenario, we effectively schedule the routes and video traffic workloads to avoid low-battery D2D outages due to non-uniform energy consumption. Specifically, we formulate the D2D cooperation as a consensus problem among the network operator and cooperative devices, and then solve it in a flexible distributed fashion. Numerical results show that the proposed framework could reach the optimality quickly in time division duplex communication systems and significantly increase the duration of the cooperative video transmission.

To increase the number of accommodated users in the cellular heterogeneous network (HetNet), we propose an energy efficient load balancing strategy for device-to-device (D2D) based relay communications. In a HetNet, user could sent data to an adjacent uncongested femtocell through the D2D-based relay communications rather than wait for the response from the congested macrocell. Specifically, the proposed strategy manages resources by taking into account both the cross-tier and the co-tier interference, and solves the maximum transmission rate problem based on D2D communications in heterogeneous network. Simulation results show that, with the guarantee of the performance of the pre-existing users, the heterogeneous network can accommodate more users and eventually achieve a higher throughput and better energy efficiency, thus the performance is significantly improved.

In this paper, we investigate the problem of minimizing energy cost for distributed Internet data centers (IDCs) in smart microgrids while taking system dynamics into consideration. Specifically, IDC operators expect to minimize the long-term energy cost with the uncertainties in electricity price, workload, renewable energy generation, and power outage state. At first, we formulate the problem as a stochastic program that captures service request distribution, server provisioning, energy storage management, generator scheduling, power transactions between smart microgrids, and main grids. Second, we use the Lyapunov optimization technique to design an operation algorithm, which enables an explicit tradeoff between energy cost saving and battery investment cost. Finally, the effectiveness of the proposed algorithm is evaluated with practical data.

In this paper, we investigate the problem of minimizing the long-term energy cost for an Internet data center (IDC) in deregulated electricity markets. Specifically, IDC operators intend to minimize energy cost by scheduling workload and battery jointly, which can fully exploit the temporal diversity of electricity price. First, we formulate a stochastic optimization problem taking heterogeneous service delay guarantees and battery management into account. Then, we design an online operation algorithm to solve the problem based on Lyapunov optimization technique. Meanwhile, we analyze the feasibility and performance guarantee of the proposed algorithm. Finally, extensive simulation results based on real-world data show the effectiveness of the proposed algorithm.

The ever-increasing demands for local area services underlaying cellular networks benefit from direct device-to-device (D2D) communications, where an efficient scheme for resource allocation is needed to increase the system capacity as the result of interference caused by spectrum sharing. Current works mainly focus on maximizing the overall transmission capacity according to interference constraints of the physical domain. However, D2D users in the social domain form different social communities, and each social community is likely to improve its own group's data transmission cooperatively without considering other communities. Therefore, social relationships among mobile users influence the strategy of the resource allocations for the D2D communications. In this paper, we first introduce social relationships in the continuum space into the resource allocation for D2D communications, which consider the complex social connections in the social domain. Then a social group utility maximization game is formulated to maximize the social group utility of each D2D user, which quantitatively measures the joint performance of social and physical domains. We theoretically investigate the Nash Equilibrium of our proposed game and further propose a distributed algorithm based on the switch operations of the resource allocation vector. Numerical results demonstrate that our proposed solution increases the utility of overall social groups about 45% on average without loss of the fairness compared with other state-of-the-art schemes.

In this paper, we propose SoCast — a cooperative video multicast framework to stimulate effective cooperation among mobile users (clients), by leveraging two types of important social ties, i.e., social trust and social reciprocity. By using SoCast, clients can form groups to restore incomplete video frames by obtaining missing packets from other clients, according to the unique video encoding structure. In return, the user perception video quality of mobile video multicast can be improved. Specifically, we first cast the problem of social ties based group formation among clients as a coalitional game, and then devise a distributed algorithm to obtain the core solution (group formation) for the formulated coalitional game. Further, a resource allocation mechanism is proposed for the base station to handle radio resource requests from client groups. Extensive numerical studies with real video traces corroborate the significant performance gain by using the SoCast.

In this paper, we investigate the problem of minimizing carbon-aware energy cost for distributed Internet data centers (IDCs) in smart microgrids. Specifically, a socially responsible IDC operator intends to jointly minimize the long-term energy cost and carbon emission in IDC operations. Since the future system parameters (e.g., electricity price, workload, renewable energy generation, and carbon emission rate) are random, we formulate the above-mentioned problem as a stochastic program to minimize the time-averaged expectation of the weighted summation of energy cost and carbon emission with guaranteed quality of service for service requests. Then, we design an operation algorithm to solve the formulated problem based on Lyapunov optimization technique without requiring any knowledge about system statistics. Finally, evaluations based on real-life data show that the proposed operation algorithm can achieve lower energy cost and carbon emission simultaneously compared with the carbon-oblivious algorithm.

In this paper, we study the problem of achieving the optimal tradeoff between operation risk and expected energy cost for Internet data center (IDC) operators in deregulated electricity markets according to the risk preferences of IDC operators. To achieve the target above, we propose a risk-constrained stochastic programming decision framework. Then, we formulate a risk-constrained expected energy cost minimization problem with the uncertainties in spot price and workload. To solve the formulated problem, we use a decomposition-based cutting plane algorithm. Finally, extensive evaluations based on real-life data show the effectiveness of the proposed decision framework.

In this paper, we propose a novel framework of cognitive radio assisted cooperation (CRAC) for downlink transmissions in orthogonal frequency-division multiple access (OFDMA) - based cellular networks. In the proposed CRAC framework, relay stations are deployed in each cell and have spectrum sensing capability. In turn, they can access unoccupied white space to opportunistically obtain additional sub-channels to assist relaying information for cellular users. One of promising novelties is that the proposed CRAC considers joint resource allocation which includes transmission mode selection, relay station allocation, and transmit power/sub-channel allocation, to cost-effectively provide services and applications. Specifically, we first formulate the joint resource allocation as a sum utility maximization problem with power constraints on the base station and relay stations, which is a mixed integer programming problem. Then, we leverage dual decomposition method and derive a centralized optimal solution. Extensive simulation results are presented and demonstrate that the proposed CRAC can achieve a significant performance improvement in terms of the downlink network throughput while maintaining comparable fairness among cellular users in contrast to the traditional relay-based cooperation approach.

To reduce the long term electricity cost of smart appliances (SAs) with deferrable operation time in smart grid, we propose a novel energy buffering framework to intelligently schedule the distributed energy storage (DES) for the cost reduction of SAs in this paper. The proposed energy buffering framework determines the action policy (e.g., charging or discharging) and the power allocation policy of the DES to provide DES power to proper SAs at proper time with lower price than that of the utility grid, resulting in the reduction of the long term financial cost of SAs. Specifically, we first formulate the optimal decision problem in the energy buffering framework as a discounted cost Markov decision process (MDP) over infinite-horizon. Then, we propose an optimal scheme for the energy buffering framework to solve the discounted cost MDP based on online learning approach. Extensive simulation results show that the proposed optimal scheme for the energy buffering framework can significantly reduce the long term financial cost comparing with the baseline schemes and the myopic scheme.

In this paper, a novel spectrum sharing scheme is proposed to improve the achievable traffic load of secondary users (SUs) under grade of service (GoS) constraints in cognitive wireless networks with heterogeneous traffic. The key idea of the proposed scheme is to introduce preemptive priority and buffering mechanism for real-time traffic and non-real-time traffic, respectively, according to their different delay characteristics. The proposed scheme can reduce the forced termination probability and the blocking probability for heterogeneous calls simultaneously. Numerical results show that the proposed scheme can effectively improve the achievable traffic load of SUs under GoS constraints.