This paper addresses the rate control issue for Video-on-Demand (VoD) services in Low Earth Orbit (LEO) satellite Internet. LEO systems employ long-distance Non-Orthogonal Multiple Access (NOMA), where the transmission rate of the last hop directly determines the Quality of Experience (QoE) levels for the VoD users and the satellite’s energy consumption. Our research identifies two primary issues: (i) determining the transmission rate to ensure high user QoE while minimizing energy consumption, and (ii) ensuring fairness among users within the satellite coverage area. To address these issues, we model the multi-user VoD viewing process as a Partially Observable Markov Process (POMDP) and describe the interactions among users using a cooperative coalition game framework. We propose Harmony, a distributed and dynamic improvement solution based on the Deep Deterministic Policy Gradient (DDPG) approach. Harmony intelligently determines each user’s transmission rate by combining feedback from user applications and MEC server metrics, ensuring superior QoE levels, energy efficiency, and fairness. The trained Harmony can be adapted to various Adaptive BitRate (ABR) algorithms, providing scalability and immediate applicability in existing LEO networks. It can also achieve improved performance in dynamic user environments. Simulation results demonstrate that Harmony improves energy efficiency and fairness, while maintaining high QoE levels and reducing MEC traffic overhead by 28.1% to 62.6%.

As one of the latest features of ultra-high-definition media services, high frame rate can significantly enhance perceptual quality, but also increases codec complexity in the transmission chain, leading to additional overhead. In this paper, we carry out comprehensive offline experiments in which the codec overhead (e.g., energy and delay) shows a linear or even quadratic increase trend with various frame rates, while correspondingly, when the frame rate increases to 75FPS, its bitrate is 24.2% lower than that under 15FPS for several scenarios. This illustrates that the overhead is more significant than the load from data traffic in the frame rate control problem. Thus, we propose a Bilateral Adaptive video Transmission framework that establishes Bilateral game-theoretic Control (BAT-BC) between sender and viewer. Through dynamically adjusting frame rate for sender and service payment for viewer, BAT-BC can flexibly adapt to the external environment such as computational state and scenario changes and it is expected to provide viewers with a smoother experience. Furthermore, we extend it to the scenario including concurrent multi-viewer and discuss the effects of grouping utility. Finally, we design a prototype system and the proposed solution is deployed on it to evaluate the performance. The frame drop rate is reduced by 61%, resulting in a 31% improvement in subjective QoE. The objective metric achieves the same level of actual experience as a fixed 60 FPS under dynamic environment.

Multipath TCP (MPTCP) technology efficiently leverages multiple paths for data transmission, achieving commendable throughput performance. However, existing MPTCP scheduling algorithms fail to meet the diverse and evolving demands of modern applications, which often extend beyond throughput to include preferences for low latency or stability. To address this gap, we introduce FlexMRS, an intelligent multi-objective MPTCP scheduling algorithm. Unlike prevailing algorithms that rely on black-box machine learning models, FlexMRS innovatively utilizes the Lagrangian relaxation method to overcome the traditional limitations of balancing multiple objectives in machine learning.Furthermore, we introduce the Krylov subspace iteration to obtain obtain the local Pareto optimal solution set, thus make FlexMRS could effectively address the switching convergence problem of different application requirement models.

The viewport in immersive media, exemplified by panoramic video, corresponds to the field of view (FoV) and significantly impacts both the volume of data transmission and the user experience. However, the instantaneous and highly dynamic nature of user interactions poses a challenge to the traditional segment-based transmission mode, creating a conflict between the need for real-time responsiveness and the structured nature of data delivery. It results in the transmission of substantial redundant data, leading to wastage of valuable resources. In this paper, we analyze data from open-source dataset and our self-collected records to investigate the interactive characteristics of viewer in immersive scenes, e.g., focus time, viewing area scope, and movement direction. Based on statistical inferences, we introduce the concept of extended field of view (EoV) to describe irregular viewing areas that mimic the moving features of the human visual field. A motion-aware tile-based viewing area adaptive control scheme, termed VAAC-IM, is designed for transmitting immersive media flexibly. We model viewing area control process as a constrained submodular minimization problem, and design a greedy search-based strategy to dynamically control irregular EoV area. Finally, we perform a comprehensive validation. The results demonstrate that VAAC-IM significantly enhances performance in terms of reducing black edge coverage, minimizing data volume, lowering latency, and improving overall user experience.

The rapid advancement in robotics technology has catalyzed the emergence of Internet of the Robotic Things crowdsourcing, a novel paradigm in the digital economy era. However, the escalating complexity of crowdsourcing tasks, coupled with the constrained resources of robot nodes and the diverse demands of stakeholders, has resulted in inefficiency, which poses a significant challenge to the burgeoning robot crowdsourcing market in IoT. To address these challenges, we develop a comprehensive analytical model that encapsulates the interests of both task scheduler nodes and robotic nodes within Internet of Things, which integrates various factors such as communication, computation, mobility, latency, and energy, thereby tailoring utility functions for different stakeholders. Secondly, we propose a Taskdriven Robotic Crowdsourcing strategy based on Hierarchical Game (TRC-HG), which conceptualizes TNs and RNs as rational entities with sequential actions. At the first layer, the interaction between participants is transformed into a Stackelberg game. This aids TNs in determining optimal pricing strategies while guiding RNs toward the most efficient task-completion strategies. Furthermore, we delve into the collaborative dynamics within the RNs, a cooperative coalition formation strategy at the second layer is established, which iteratively determines node responsibilities and coalition members. The Nash stability and optimality of coalition are ensured, thereby maximizing the utility of RNs. Finally, we validate the performance through a series of high-fidelity simulation experiments. These experiment results, benchmarked against classical methods, highlight significant improvements in terms of latency, energy consumption, and cooperative efficiency.

As a key enabling technology in intelligent heterogeneous Internet of Things (IoT), edge caching provides important support for reducing core network load and improving network service efficiency, especially for high bandwidth demand services represented by multimedia applications. However, external time-varying information is hard to be obtained comprehensively in a complicated heterogeneous IoT environment. Meanwhile, there exists the substitutability of content (e.g., videos with different bitrates), which is difficult to make caching decisions online in real-time to achieve fast feedback with low latency and avoid useless deployment. To this end, this article designs a transcoding-enabled online cache scheme for IoT video service with cloud–edge–terminal collaboration. First, we design a variable bitrate video routing strategy to dynamically retrieve content from cloud/edge according to user demands. Furthermore, the video caching problem is considered as an online convex optimization problem to learn utility gradient and determine the optimal caching strategy in real-time without any prior information. On this basis, we extend the problem to elastic networks with dynamic available resources and prove the sublinear regret and sublinear constraint violation. Finally, we summarized five video request data sets and carried out differentiated multiple verifications based on different request habits and content requirements. Compared with the most advanced algorithms in terms of delay, we evaluated the performance advantages of the proposed scheme.

Multi-access Edge Computing (MEC) has revolutionized the delivery of large-scale mobile multimedia services by endowing network edge with computing and caching capabilities. This not only relieves the load on core networks, but also significantly reduces data access latency. However, deploying edge data centers with a high density to accommodate the growing demand for multimedia services is not cost-effective. With the rapid development of the Internet of Things (IoT) industry, recent studies have shown that by allowing UAVs with integrated computing and communication to form a Mobile Device Cloud (MDC) environment via UAV-to-UAV (U2U) communications in IoT networks, UAVs can play an important role in assisting cellular networks with multimedia delivery and providing excellent service for IoT devices on the ground. While a MDC environment composed of UAVs offers flexibility and cost-effectiveness, the challenge remains in allocating caching resources in a timely manner to meet the dynamic content demands. To address this challenge, we design a novel Mobile Device Cloud-enabled Caching (MDC) framework, which makes use of the available caching and U2U communication capabilities to enable any UAV to obtain dynamically content from other nearby UAVs via the IoT network. By modeling the dynamic network status as a fluid-based system, MDC employs a dynamic caching allocation algorithm to minimize both service latency and caching costs. Extensive experiments demonstrate that MDC outperforms a state-of-the-art MDC multimedia delivery approach by improving average cache utilization with over 40% and reducing average access latency with more than 25%.

Fairness-aware federated learning (FFL) plays a crucial role in mitigating bias against specific demographic groups (e.g., gender, race, occupation) during collaborative training. Along with the ever-emerging new attack paradigms like gradient leakage and model poisoning, the reliability of FFL also obtains lots of research attention. Either UAV nodes or FFL aggregators could be untrusted adversaries. Although multiple security mechanisms involving encryption, obfuscation, Byzantine-robustness, and detection have been proposed, concrete to UAV networks, the majority of existing solutions are unfeasible due to high heterogeneity and limited resources among participants. Hence, in this paper, we propose mutually reliable FFL (MR-FFL), a stratified community-based framework to facilitate privacy protection (FFL aggregator’s reliability) and poisoning elimination (client nodes’ reliability) jointly for FFL in heterogeneous UAV networks. We first divide UAV nodes into both peer communities (PC) and colleague communities (CC) according to cross-participant similarity and task-oriented fitness, respectively. Thus, the arbitrarily settled learning tasks following fair principles can be efficiently completed by fine-tuned colleague communities, even in the presence of a large degree of heterogeneity among peer communities. Then, we integrate community-specific differential privacy into the MR-FFL process, to achieve privacy amplification as well as efficient and personal collaborative training at the same time. More importantly, we proposed a community-based credit evaluation to resist poisoning attacks in heterogeneous environments. The results on several standard datasets also highlight the performance of MR-Fed in terms of fairness, accuracy, and integrity jointly.

Software defined Internet of Vehicles (SD-IoV) is an emerging paradigm for accomplishing Industrial Internet of Things (IIoT). Unfortunately, SD-IoV still faces security challenges. Traditional solutions respond after attacks happening, which is low-effective. To cope with this problem, moving target defense (MTD) was proposed to modify network configurations dynamically. However, current MTD for IIoT has several drawbacks: 1) it cannot handle highly dynamic environments; 2) MTD strategy lacks intelligence because it needs attack–defense models; 3) they are difficult to trace sources. In this article, we propose an intelligent MTD scheme to defend against distributed denial-of-service in SD-IoV. Firstly, we model the configuration mutation of roadside units as a Markov decision process (MDP), and adopt deep reinforcement learning to solve the optimal configuration. Next, we evaluate the trust of vehicles after shuffling, which can distinguish spy vehicles. Finally, extensive simulation results confirm the effectiveness of our solution compared with representative methods.

The latest evolution of wireless communications enables user access rich Virtual Reality (VR) services via the Internet, including while on the move. However, providing a premium immersive experience for massive number of concurrent users with various device configurations is a significant challenge due to the ultra-high data rate and ultra-low delay requirements of live VR services. This paper introduces an innovative multi-user cost-efficient crowd-assisted delivery and computing (MEC-DC) framework, which leverages mobile edge computing and end-user resources to support high performance VR content delivery over 5G-and-beyond heterogeneous networks (5G-HetNets). The proposed MEC-DC framework is based on three main solutions. First is a novel buffer-nadir-based multicast (BNM) mechanism for VR transmissions over 5G-HetNets. BNM ensures smooth and synchronized user viewing experience by maximizing the average playback buffer-nadir of all participants with stochastic optimization. Second and third are practical distributed algorithms: the cost-efficient multicast-aware transcoding offloading (MATO) and crowd-assisted delivery algorithm (CAD) which optimize jointly multicast delivery and video transcoding. The algorithms optimality and complexity were investigated. The proposed MATO-CAD solution was evaluated with real datasets, trace-driven numerical simulations, and prototype-based experiments. The trace-driven experimental results showed how the proposed solution provides 18% throughput improvement, lowest delay and best playback freeze ratio in comparison with three other state-of-the-art solutions.

Providing premium panoramic livecast services to worldwide viewers considering their ultra-high data rate and delay-sensitivity is a significant challenge in the current network delivery environment. Therefore, it is important to design an efficient way of improving viewer quality of experience while conserving bandwidth resources. In this context, this paper introduces a novel cost-efficient federated transmission framework calledFedLiveand a set of algorithms to support it. First a gradient-based clustering method is proposed to group the geo-distributed viewers with similar viewing behavior into content delivery alliances by exploiting the geometric properties of the gradient loss. Next, aReinforcedVariationalInference (RVI) structure-based approach is proposed to assist with the collaborative training of the viewer field of view (FoV) prediction model while also accelerating the tile delivery process. A novel prediction-based asynchronous delivery algorithm is designed in which both the high accuracy FoV prediction and efficient live 360$^\circ$video transmission are achieved in a decentralized manner. FedLive was implemented for testing and an open source code is made available. Finally, the proposed solution was evaluated against a benchmark and three alternative state-of-the-art solutions using a real-world dataset. The experimental results show that our approach provides the highest prediction accuracy, better service performance, and saves bandwidth when compared with the other solutions.

Intel software guard extensions (SGX) technology allows cloud vendors to provide customers with an independent and trusted execution environment (TEE). It protects critical data confidentiality and integrity from malicious software. However, more and more SGX side-channel attacks have appeared, which seriously undermine the confidence of tenants in cloud security. The related research focuses on system hardware and SGX compiler solutions for specific attacks, which also has difficulties in deployment. Differently, we propose an intelligent-driven proactive defense strategy, which is based on live migration. To the best of our knowledge, this is the first proactive defense against SGX side-channel attacks. We adopt the Markov decision process to solve the migration programming problem. The innovative deep reinforcement learning (DRL) solves problems of the unknown state transition probability and large machine load states, which is called self-checking proximal policy optimization (SPPO). It changes the reward pattern, improving the convergence speed and stability of DRL. In prototype experiments, we deploy the strategy in the OpenStack platform agilely to prove the defense performance and low virtual machine costs.

Virtual reality (VR) content, including 360° panoramic video, provides users with an immersive multimedia experience and therefore attracts increasing research and development attention. However, the requirement of high bandwidth and low latency of virtual reality service demand puts forward greater challenges to the current infrastructure, especially mobile networks. Inspired by the sharable nature of virtual reality content tiles, we further considered the potential opportunities for computing, caching, and multicast to address the challenges of transmission of panoramic content. This paper proposes a novel transcoding-enabled VR video caching and delivery framework for edge-enhanced next-generation wireless networks. Firstly, an edge cooperative caching scheme based on multi-agent reinforcement learning is introduced to improve the utilization efficiency of computing and storage resources, and then reduce service delay. Second, a two-tier NOMA-based base station-multicast group matching mechanism is designed to solve the collaboration challenge during the edge delivery process. A series of experiments have demonstrated the advantages of the proposed scheme in terms of cache hit rate, latency and other aspects in comparison with alternative approaches.

Computational offloading, as an effective way to extend the capability of resource-limited edge devices in Internet of Things (IoT), is considered as a promising emerging paradigm for coping with delay-sensitive services. However, on one hand, applications commonly include several subtasks with dependent relations and on the other hand, the dynamic changes in network environments make offloading decision-making become a coupling and complex NP-hard problem, difficult to address. This paper proposes an intelligent Computational Offloading scheme for Dependent IoT Application (CODIA), which decouples the performance enhancement problem into two processes: scheduling and offloading. First, a prioritized scheduling strategy is designed and its complexity is analyzed. Then, an offloading algorithm with offline training and online deployment is introduced. Due to the temporal continuity between subtasks, the dependency relation is transformed into a transition of device state, and the overhead for the whole application is considered to be the long-term benefit.CODIAleverages an Actor-Critic-based solution, where the IoT devices are able to deploy intelligent models and dynamically adjust the offloading strategy to achieve low latency, while controlling energy consumption. Finally, a series of experiments are conducted to verify the robustness and efficiency of the proposed solution in terms of convergence, latency, and energy consumption.

With the development of communication technology, emerging multimedia (e.g. virtual reality) can provide users with more immersive service experience. However, due to the ultra-high rendering and splicing requirements of multimedia content, the higher demand for computing resources is put forward for the playback device. The anomalies of energy consumption and latency caused by such computationally intensive tasks hinder the practical application of emerging multimedia technology in mobile networks. In this regard, this paper proposes an edge computing assisted multimedia service optimization scheme (ECMSO) to broaden the computing capacity of the viewer(i.e. requester), so as to ensure that content can be served in time and reduce the energy cost of computation from the perspective of executor and requester, respectively. First, a computational offloading scheme based on deep reinforcement learning is designed. It optimizes intelligently the energy consumption while meeting the latency requirements of the requester. Secondly, a heuristic algorithm to allocate power, bandwidth, and computing resources for candidate executors is proposed. Finally, a series of simulation experiments are conducted to demonstrate the effectiveness of our proposed scheme.

Recent years have witnessed an outstanding increase in popularity of Crowdsourced Livecast Services (CLS), which is the latest trend in social media. In CLS, transcoding enormous video contents from massive broadcasters and providing high-quality CLS for global viewers with heterogeneous devices are computation-intensive as well as time-consuming. There are some schemes that design viewer-assisted transcoding scheme, but it is challenging to achieve an efficient and fair task assignment due to the dynamic of computing and communication resources. This paper introduces a viewer-assisted CLS framework and focuses on proposing an innovative fairness-guaranteed task assignment scheme, which is a key challenge in this context. Considering the dynamic nature of viewers’ computing and communication resources and stability, a dynamic programming problem with fairness and QoS constraints is formulated. To solve the problem, we devise a Fair Bandit (FB) algorithm based on the Combinatorial Multi-Armed Bandit (CMAB). Finally, the effectiveness of proposed scheme is demonstrated by trace-driven simulations.

Intensive video transcoding and data transmission are the most crucial tasks for large-scale Crowd-sourced Livecast Services (CLS). However, there exists no versatile model for joint optimization of computing resources (e.g., CPU) and transmission resources (e.g., bandwidth) in CLS systems, making maintaining the balance between saving resources and improving user viewing experience very challenging. In this paper, we first propose a novel universal model, called Augmented Graph Model (AGM), which converts the above joint optimization into a multi-hop routing problem. This model provides a new perspective for the analysis of resource allocation in CLS, as well as opens new avenues for problem-solving. Further, we design a decentralized Networked Multi-Agent Reinforcement Learning (MARL) approach and propose an actor-critic algorithm, allowing network nodes (agents) to distributively solve the multi-hop routing problem using AGM in a fully cooperative manner. By leveraging the computing resource of massive nodes efficiently, this approach has good scalability and can be employed in large-scale CLS. To the best of our knowledge, this work is the first attempt to apply networked MARL on CLS. Finally, we use the centralized (single-agent) RL algorithm as a benchmark to evaluate the numerical performance of our solution in a large-scale simulation. Additionally, experimental results based on a prototype system show that our solution is superior in saving resources and service performance to two alternative state-of-the-art solutions.

Nowadays, amateur broadcasters can massively generate video contents and stream them across the Internet. For this reason, crowdsourced livecast services (CLS) are attracting millions of users around the world. To provide a smooth and high-quality playback experience to viewers with diversified device configurations in dynamic network conditions, CLS providers have to find a way to deploy cost-effective transcoding operations by distributing the computation-intensive workload among Cloud, Edge, and Crowd. In addition, it is necessary to control transcoded streams from million broadcasters to worldwide viewers. To address these challenges, we propose a novel stochastic approach that jointly optimizes the usage of transmission resources (e.g., bandwidth), and transcoding resources (e.g., CPU) in CLS systems that leverage the cooperation of Cloud, Edge, and Crowd technologies. In particular, we first design an augmented queue structure that can jointly capture the dynamic features of data transmission and online transcoding, based on the virtual queue technology. Then, we formulate a joint resource allocation problem, using stochastic optimization arguments, and devise an Accelerated Gradient Optimization (AGO) algorithm to solve the optimization problem in a scalable way. Moreover, we provide four main theoretical results that characterize the algorithm’s steady-state queue-length, optimality, and fast-convergence. By conducting both numerical simulations and system-level evaluations based on our prototype, we demonstrate that our solution provides lower system costs and higher QoE performance against state-of-the-art solutions.

By exploiting the massive data generated from the numerous interconnected machines and control systems, industrial Internet-of-Things (IIoT) provides unprecedented opportunities for facilitating the intelligence and smartness of manufacturing. Timely processing the large-scaled IIoT data by the conventional computation framework, such as Cloud computing, however, is nontrivial due to its costly resource usage, intolerable delay, and unbearable backbone pressures. By leveraging the idle resources of smart objects at the edge, mobile device cloud (MDC) becomes promising for the IIoT data analysis, thanks to the flexible resource provision and nearby task offloading. However, MDC workers are mostly human-carried devices with large scale, high dynamic resource provision, and untruthful behaviors, which pose significant challenges on MDC task allocation. In this article, we propose a blockchain-based decentralized and truthful framework for MDC (BC-MDC). BC-MDC enables the decentralization and prevents dishonesty by incorporating a plasma-based blockchain into the MDC. We design four smart contracts for distributedly managing the worker registration, task posting/allocation, rewarding, and penalizing. Furthermore, MDC task allocation is formulated as a stochastic optimization problem that jointly minimizes the long-term processing cost and risk of task failing. We also design a truthful reward/penalty algorithm that stimulates workers to provide resources and enforce them to keep the promise as well. Collaborated by the extensive simulation tests, we show how our proposed scheme achieves low cost on usage and high truthfulness and outperforms state-of-the-art solutions.

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

By pushing computing functionalities to network edges, backhaul network bandwidth is saved and various latency requirements are met, providing support for diverse computation-intensive and delay-sensitive multimedia services. Due to the limited capabilities of edge nodes, it is very important to decide which services should be provided locally. This paper investigates the cloud-edge service offloading problem. Different from prior works which only give the proportion of computation offloading with constraint of computing capacity, we also take the storage space into account and determine the computing status of each service. We formulate the problem as a Markov decision process whose goal is to maximize the long-term average reduction of delay. The problem is hard to be solved with traditional methods because of the extremely large action space and lack of information about transition probability. Instead, this paper proposes an innovative deep reinforcement learning method to solve it. The proposed multi-update reinforcement learning algorithm introduces a novel exploration strategy and update method, which reduce dramatically the size of the action space. Extensive simulation-based testing shows that the proposed algorithm has fast convergence and improves the system performance more than other three alternative solutions do.

The edge computing-based 5G networks have the advantages in efficiently offloading the large-scale Internet traffic, which is considered to be a promising architecture to alleviate the conflict between transmission performance and quality of experience (QoE). However, due to the unreliability of service providers and the mutual interference between wireless channels in 5G networks, it is still difficult for existing solutions to provide satisfactory multimedia services for mobile users. In response to these crucial challenges, this paper proposes a reliable and efficient multimedia service optimization framework named “REMSO” hereby, including a two-stage joint optimization procedure. Specifically, a reliable video service mechanism is first constructed to help the mobile users distinguish the credible and economic service BSs. Afterwards, an efficient wireless resource allocation strategy is established to achieve low latency and energy efficient video service optimization. In particular, the Stackelberg and potential game models are leveraged to achieve these optimization objectives. Finally, extensive simulations corroborate that our REMSO framework can deliver prominent performance advantages in terms of the reliability and efficiency when comparing with the state-of-the-art solutions.

The many fold capacity magnification promised by 5G vehicular networks will likely provide massive multimedia services, including infotainment, augmented reality, location services, etc. However, the large-scale and stochastic characteristic of these burgeoning multimedia applications will lead to an exponential increase of traffic in vehicular networks. Meanwhile, the diversified requirements introduced by the coexistence with traditional services will also bring new challenges to the efficient usage of resources. To cope with the above challenges, we propose a novel Stochastic Optimization framework for Pricing-aware Multimedia Services (SOPMS) in this paper, which targets the maximization of utility with the constraints of system stability and traffic pricing policy. Specifically, we leverage the Lyapunov function to address this optimization objective, which is decomposed into three tractable subproblems. For each problem, a distinct algorithm is conceived, i.e. Quality of Experience (QoE) based utility maximization, cooperative resource allocation and pricing-based transmission control. Finally, validated by the simulations, our proposed SOPMS preserves the optimality and significantly improves the queue stability and service utility, in comparison with other state-of-the-art solutions.

As an important scenario under the 5G, the Internet of things (IoT) is undertaking countless computing tasks, which are obtained from real life. However, for IoT devices, due to the limited computing resource and battery capacity, it is difficult to cope with the diversified incoming computing tasks. To this end, this paper studies the IoT task computing expenditure problem with the assistance of fog computing and cloud center. We firstly propose a game theoretic task computing framework (GTTC) to ease the competition of multi-nodes by taking into account the expected benefit of each computing node. Then, we put forward the concept of tendency-oriented priority (TOP) to coordinate the scheduling order between multi-tasks of fog computing node for further reducing the expenditure. Finally, the effectiveness of the proposed mechanism is verified by sufficient experimental simulations in terms of a wide set of performance metrics.

Multipath TCP (MPTCP) can take advantage of multiple paths to transmit data and has been deeply optimized by many researchers. However, most researchers only devote themselves to improve the transmission performance, neglecting the price cost which is another factor that users are concerned about. This paper proposes a novel stochastic optimal scheduler for MPTCP (SOS-MPTCP) that utilizes Lyapunov optimization technique in software defined wireless network (SDWN). SOS-MPTCP analyzes and solves the trade-off problem between the performance and price cost from users' perspective. Besides, with the help of centralized optimization in SDWN architecture, the controller can feed status information of each path back to mobile terminals for SOS-MPTCP to make decisions. SOS-MPTCP includes three control decisions: 1) packets admission control; 2) packets distribution control; 3) data traffic purchasing control. SOS-MPTCP aims to maximize the throughput and minimize the price cost for users. Experiment results have proved the efficiency of trade-off optimization and the transmission system can achieve the expected stability.

Caching has advantages in mitigating the backhaul data traffic and multicast is able to satisfy multiple identical requests by a multicast stream, which are the two most promising technologies to realize tremendous data transmission in 5G networks. However, many studies focus on cooperative caching but ignore the problem that what contents to multicast for a given caching status by cooperation between base stations (BSs). In this paper, we consider the cooperative multicast scheduling problem in cache-enabled 5G networks to satisfy user demands while minimizing the energy consumption. We propose a novel pending request queue model and transform the cooperative multicast scheduling problem into a Lyapunov stochastic optimization problem that can be calculated on-line. By analyzing properties of the problem, we proposed an on-line centralized algorithm to obtain the optimal strategy. Motivated by practical deployment, we further propose a distributed algorithm which has similar performance and lower complexity. Extensive simulations have been conducted to verify that our algorithms have better performance than several state-of-art algorithms, including both energy consumption and delay.

In the multimedia cloud platform, the resource scheduling performance directly affects the energy consumption, resource utilization of the active physical machine (PM) and virtual machine (VM) security. Besides, service level agreement (SLA) violation rate also fluctuates with the strategy. Many optimization methods have been launched to cope with this scheduling task, while none of them accommodate all the above aspects in a uniform manner to our best knowledge. In this paper, aiming at optimizing the four sides performance, we propose a new resource scheduling strategy called Security Enhanced Particle Swarm Optimization (SE-PSO) based on VM migration which uses Particle Swarm Optimization (PSO) as a kernel part. Firstly, the inertia factor and the learning factor are dynamically adapted to improve the search performance of SE-PSO. Then, by periodically predicting physical hotspots with the exponential smoothing model, we reduce unnecessary migrations and thus minimize the VM migration security risk. Finally, roulette wheel idea is applied to achieve long-term optimization of the platform resources. The experiments conducted in CloudSim with real-world dataset also show that SE-PSO has a good overall performance in energy consumption, resource utilization, SLA violation rate and migration security compared with the mainstream PSO algorithm.

Due to its advantages in service flexibility, scalable video coding (SVC) has been widely used in Device-to-Device (D2D) network communication, which is an effective network technology in the fifth generation (5G) communication network. However, duo to the limited capacity of mobile device, the uninterrupted transmission communication is difficult to be maintained, which needs to be solved urgently. Therefore, in this paper, we introduce the maximum offloading traffic and energy cost ratio to estimate service performance and propose a heuristic algorithm based on the greedy strategy to maximize the offloading traffic because of the NP-hardness of the cache placement problem for SVC. Then, we improve the energy efficiency over D2D link by optimizing the transmission power. Finally, a series of detailed simulation experiments are conducted to analyze the relationship between offloading traffic and energy cost, which demonstrates that a tradeoff exists between the significant amount of traffic and approving energy efficiency.

Multipath TCP (MPTCP), as a promising transmission protocol, can aggregate the bandwidth of multiple paths in order to improve the transmission rate. However, due to the lack of perceiving the network status from lower layers, MPTCP cannot adaptively adjust the number of subflows, which will lead to network congestion or underutilization of network resources. Besides, plenty of packets will be out-of-order severely due to the diversity among the paths so that transmission performance degrades significantly. To address the problems mentioned above, we propose a novel QoS-driven and SDN-assisted MPTCP path selection scheme (QSMPS) for high-quality transmission service. QSMPS utilizes a scalable SDN-assisted approach to monitor and analyze network status information. Through matching service demand and the provided capacity of current network, the scheme calculates the optimal number of subflows, then distributes them to the least differential delay paths determinately. Simulation results show QSMPS outperforms the existing solutions by evaluating performance in Mininet emulator and Ryu controller.

Information-centric networking (ICN) is of high interest to the Internet of Things (IoT) community, since the dissemination of massive data continuously produced by IoT devices can be easily handled by ICN’s data naming scheme and inherent multipath delivery. Providing optimal multipath-oriented transmission control is crucial for ICN-IoT data delivery, but yet remains challenging because of the randomness of request arrival, dynamic link condition, and on-path caching. More prominently, the resource limitation and scalability issues in IoT require the control scheme to be lightweight and distributed. In this paper, we propose a distributed stochastic optimization framework for multipath transmission control in ICN-IoT. The transmission control, including request scheduling and data rate regulation, is formulated as a stochastic concave optimization problem, which aims to accommodate the randomness, unpredictability, and multipath delivery of ICN-IoT and maximize the overall throughput. This problem is linearly separated into two subproblems: 1) a request scheduling problem and 2) a data rate control problem, which can be individually solved per time slot. A distributed alternating descent method (DADM) is designed to optimally control the transmission by solving the aforementioned problems at client sides. DADM enables each client to sequentially update the request schedule and rate regulation via communicating the links and providers they use, which asymptotically converges to optimality while allowing low-complexity and decentralized implementation. Validated by simulations, our DADM significantly improves throughput, delay reduction, and energy efficiency, in comparison with other state-of-the-art solutions.

The ever fast growing mobile social video traffic has motivated the urgent requirement of alleviating backbone pressures while ensuring the user-quality experience. Mobile video prefetching previously caches the future accessed videos at the edge, which has become a promising solution for traffic offloading and delay reduction. However, providing high performance prefetching still remains problematic in the presence of high dynamic mobile users' viewing behaviors and consecutive generated video content. Besides, given the fact that making prefetching decision requires viewing history that is sensitive, the increasing privacy issues should also be considered. In this paper, we propose a differential privacy oriented distributed online learning method for mobile social video prefetching (DPDL-SVP). Through a large-scale data analysis based on one of the most popular online social network sites, WeiBo.cn, we reveal that users' viewing behaviors have strong a relation with video preference, content popularity, and social interactions. We then formulate the prefetching problem as an online convex optimization based on these three factors. Furthermore, the problem is divided into two subproblems, and we implement a distributed algorithm separately to solve them with differential privacy. The performance bound of the proposed online algorithms is also theoretically proved. We conduct a series simulation based on real viewing traces to evaluate the performance of DPDL-SVP. Evaluation results show how our proposed algorithms achieve superior performance in terms of the prediction accuracy, delay reduction, and scalability.

The manyfold capacity magnification promised by dense 5G networks will make possible the provisioning of broadband multimedia services, including virtual reality, augmented reality, and mobile immersive video, to name a few. These new applications will coexist with classic ones and contribute to the exponential growth of multimedia services in mobile networks. At the same time, the different requirements of past and old services pose new challenges to the effective usage of 5G resources. In response to these challenges, a novel Stochastic Optimization framework for Green Multimedia Services named SOGMS is proposed herein that targets the maximization of system throughput and the minimization of energy consumption in data delivery. In particular, Lyapunov optimization is leveraged to face this optimization objective, which is formulated and decomposed into three tractable subproblems. For each subproblem, a distinct algorithm is conceived, namely quality of experience--based admission control, cooperative resource allocation, and multimedia services scheduling. Finally, extensive simulations are carried out to evaluate the proposed method against state-of-art solutions in dense 5G networks.

As one of the key technologies for future 5G, Device- to-Device communications (D2D) offloads traffic to local by enabling mobile equipment directly communicating with each other, which perfectly supporting distributed applications and IoT scenarios. Integrating Information-centric networking (ICN) with D2D is becoming an attractive trend because of the superior advantages of inherent support of caching and name-based routing. Nevertheless, efficient caching in ICN D2D still remain problematic due to the low utilization of caching space and multicast feature of wireless scenarios. In this paper, we propose a novel optimal coded content caching mechanism for ICN-based 5G D2D. We first building a fluid-based model to describe how the roles of mobile nodes evolve with the user behaviors and caching strategy. We then accordingly formulate the coded caching problem as an optimization problem, which mainly considers the tradeoff between delivery latency and energy consumption. The existence of optimal solutions is proved theoretically. We further propose a Learn Tree- based Code Content (LTCC) mechanism to cluster the contents for content coding selection and an Optimal Coded Content Caching (O3C) algorithm to solve coded content caching problem. Finally, we conduct massive simulation tests to validate the performance of the proposed algorithm against the state-of-art solutions.

The recent fast development of wireless communications and smart devices has opened the avenue to supporting high quality video streaming services in vehicular networks. This growing trend towards enhanced video services and the inefficient content distribution of conventional IP networks have motivated the researchers to propose new Internet architectures that are more efficient for content distribution in general and in vehicular networks in particular. Information-centric networking (ICN) shifts the network paradigm from host centric to content centric, providing effective content distribution by named-based routing and in-network caching, which becomes a promising solution for sharing video streaming among vehicles. In this paper, we present a novel Family-Aware Pricing Strategy (FAPS) to accelerate video streaming dissemination over Information-Centric Vehicular Networks (ICVNs). We first classify the mobile users into multiple families by investigating user similar behaviors. Based on the family, an efficient video sharing scheme is proposed to support near end video fetching. In addition, a pricing-based video caching policy is also proposed to accurately optimize caching distributions. Simulation results show how our proposed strategy achieves better performance than other state-of-art solutions in terms of caching hit ratio, searching delay, freeze times and control overhead.

As an extension of Transmission Control Protocol (TCP), Multi-Path Transport Control Protocol (MPTCP) provides a reliable and streaming-oriented transmission service to the upper applications. However, when turning to the real-time multimedia transmission, the repeatedly retransmission of expired segments is unnecessary and inefficient. Thus, we propose a Message-Oriented Partial-Reliability (MO-PR) improvement for MPTCP in this paper. The MO-PR firstly extend the Partially-Reliability transmission scheme to MPTCP which allows the sender to abandon the invalid segment by notifying the receiver. Then, the Message-Oriented retransmission mechanism is designed to improve the discarding efficiency. Finally, the comparison-based simulation results show that MO-PR can effectively improve the transmission performance of multimedia in dynamic wireless networks.

Device-to-Device (D2D) communications are a prominent feature of 5G systems, introduced to provide a native support to distributed services in mobile environments. D2D technologies enable straight interactions between mobile terminals without a compulsory involvement of base stations. In this manuscript, we study and propose an optimized caching strategy to content distribution on top of D2D technology, based on Information Centric Networking (ICN) principles. The rationale is that ICN architectures can provide seamless support to mobile services and decouple contents from node identifiers, thus providing a promising match with D2D requirements. To this end, a novel fluid-based model in proposed hereby that catches the interplay between ICN functionalities, D2D requirements, and 5G specifications. Then, based on this model, an optimal content replication problem is formulated, encompassing caching overhead and system load. Additionally, this problem is thoroughly analyzed to prove that it has an optimal solution with time threshold form. A practical algorithm ς*-OCP is further proposed in order to implement the optimal caching control in realistic environments. Finally, a massive simulation campaign is carried out to test the proposed algorithm in comparison to state-of-the-art solutions.

Dynamic characteristics of user interactivity make supporting VCR-like operations in peer-to-peer (P2P) Video on Demand (VoD) streaming systems very challenging. Recently, the prediction-based prefetching of hot segments scheme has emerged as a promising approach to improve user Quality of Experience. However, this prediction model uses a centralized server to collect and analyze the large volumes of user viewing logs for predicting user VCR behavior. This log server can easily become bottleneck in terms of data exchange and processing. In this paper, we propose a novel distributed load balancing and VCR-aware two-tier P2P VoD System (DLCA). DLCA relies on a two-tier architecture. In the low tier, the common nodes form a classic gossip-based unstructured network for normal data distribution. On the top layer, a portion of strong nodes establish a structured DHT network for VCR-related information analysis and publish. By employing a pattern mining algorithm, each strong mode maintains a prefetching routing table, which can effectively assist common nodes prefetching segments for VCR-like interactivity during playback. Simulation results show how DLCA outperforms a state of the art centralised method in terms of performance.

In this paper, we present a novel state-based fault-localization approach called DelFal. Assuming the availability of the execution trace which leads to the reported program execution failure, this new approach successively selects sets of trace points to allow the performance of efficient automatic explorations on program execution states in order to help the developer locate programming faults responsible for the observed execution failure. With each of such sets of trace points, the program state at each trace point is symbolically altered, by negating a certain atomic predicate, to see whether the same failure occurs with symbolic execution continuing from the corresponding program point in the source code. The set of trace points is chosen such that the union of the program states is of the minimum size among all candidate sets. Such a set of trace points is called a minimum debugging frontier set (abr. MDFS). Depending on the result from the symbolic execution, the next MDFS is determined by moving forward or backward on the remaining program trace. This process of trace shortening goes on until the offending faulty code is found. The MDFS approach requires the execution failing location to be provided, but the specification of the desired program state is optional. With such specification, it may achieve a more accurate fault report. To evaluate our approach, we tried it on 15 real bugs from real world programs. Results show that our approach is effective in explaining failures within reasonable time.