Sequential federated learning (SFL) trains models collaboratively across clients in a chain manner. This order shows communication efficiency compared to traditional FL in a parallel manner with a star topology. However, SFL can fail to produce stable training results when clients have significant statistical heterogeneity among their local data distributions. To address these challenges, we propose a novel element-wise model decoupling framework namedSeqFedEDTthat accelerates SFL training by separating model parameters of each client into a shared subset for global knowledge collaboration and a personalized subset for migrating data heterogeneity. We explore three types of parameter contribution scoring metrics based on gradient, Fisher information, and parameter importance (PI) for personalized parameter selection. In addition, we propose a quantile-based thresholding mechanism to separate shared and personalized subsets and explore the best performance quantile selection in numerical studies. Extensive experiments demonstrate thatSeqFedEDToutperforms eight state-of-the-art methods across diverse datasets and heterogeneity scenarios. All code and results are available athttps://github.com/tian0920/SeqFedEDT.

The dynamic nature of Low Earth Orbit (LEO) satellite networks introduces unique challenges to satellite edge computing (SEC), particularly regarding handover (HO) strategies for computational task continuity. Conventional HO strategies fail to account for computational task urgency, leading to task timeouts and reduced performance. This paper presents a Risk Predictive Proactive Handover (RPP-HO) mechanism tailored for SEC environments. Unlike conventional reactive approaches, RPP-HO enables satellites to proactively initiate handovers by predicting task timeout risks based on queue congestion and deadline constraints. A multi-step methodology incorporating task-level risk factor evaluation, greedy user selection, and adaptive locking mechanisms ensures task success without excessive handover frequency. Simulation results using real Starlink constellation data demonstrate that RPP-HO improves task success rates by effectively offloading at-risk tasks while maintaining system stability under varying workloads. The proposed strategy offers a lightweight, computation-aware enhancement to standardized Conditional Handover procedures in 6G Non-Terrestrial Networks (NTN).

360-degree video streaming faces a fundamental trade-off between FoV prediction accuracy and playback buffer management, known as the Field of View (FoV) prediction-buffer dilemma. Existing solutions suffer from inadequate temporal modeling and decoupled bitrate decision-making, leading to suboptimal Quality of Experience (QoE). This paper proposes TF-PPO, a novel reinforcement learning framework that systematically addresses this challenge through three key innovations: (1) A multi-scale temporal feature extractor combining Temporal Convolutional Networks (TCN) with attention mechanisms to capture both short-term fluctuations and long-term patterns in network/viewing behaviors; (2) A coupled Adaptive BitRate (ABR) decision maker employing self-attention pooling to jointly optimize bitrate selection, buffer dynamics, and prediction reliability; (3) A multi-objective Proximal Policy Optimization (PPO) framework that explicitly balances video quality, rebuffering avoidance, and quality stability. We introduce the FoV Prediction to Playback (FPTP) delay metric to quantify the temporal coupling between the prediction horizon and buffer states. Extensive evaluations using real-world network traces and FoV datasets demonstrate that TF-PPO achieves higher QoE and shorter FPTP delay compared to state-of-the-art baselines, while maintaining superior temporal consistency in bitrate selection.

The widespread deployment of Large Language Models (LLMs) is often constrained by the significant computational and memory demands of the inference process. A critical bottleneck in distributed serving systems arises from the redundant processing of requests that share common prefixes, such as system prompts or few-shot examples. Traditional contentagnostic load balancers fail to exploit these redundancies, leading to inefficient resource utilization and increased latency. This paper introduces a dynamic, prefix-aware request scheduling system designed to optimize distributed LLM serving. Our approach intelligently routes incoming requests to specific GPU workers by analyzing prompt content and matching it with the resident Key-Value (KV) caches across the cluster. By colocating requests with shared prefixes, our system maximizes KV cache reuse, minimizes expensive prefill computations, and enables more efficient batched attention operations at the worker level. We implemented and evaluated this scheduler on a 12 -node GPU cluster using a real-world chatbot workload. The results demonstrate the profound impact of content-aware scheduling: our system increased the aggregate prefill throughput by over 144 % and reduced the median Time-to-First-Token by over 40 % compared to a conventional Round-Robin policy. These performance gains are a direct result of an 82 % relative increase in the prefix cache hit rate, validating our approach as a highly effective and cost-efficient strategy for enhancing the throughput and responsiveness of large-scale LLM services.

Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly susceptible to adversarial attacks, which generate malicious traffic to evade NIDS detection. Existing approaches often adapt adversarial attacks from computer vision (CV) tasks to the NIDS domain, overlooking the fundamental differences between CV and NIDS. This results in two major issues: 1) The generated network traffic may become invalid, 2) The generated traffic may lose its original attack semantics. To address these issues, this paper proposes an adversarial attack specifically designed for NIDS. Instead of directly generating flow-level features, our approach incrementally generates packet-level features to construct adversarial traffic. During the generation process, the semantic integrity of the traffic is monitored at each stage, effectively avoiding the issues of invalid traffic and semantic loss observed in existing methods. We evaluate our attack algorithm against current NIDS models using the CIC-UNSW-NB15, CIC-DDoS2019, and CIC-IDS-2017 datasets. The proposed method achieves an average evasion success rate of 92.78%, while ensuring that the generated adversarial traffic remains semantically consistent with the original malicious traffic.

Federated Learning (FL) is an emerging distributed learning framework designed to address isolated data island and protect privacy. Besides, Clustered Federated Learning (CFL) is introduced as an efficient multitask scheme to solve heterogeneous problems in FL where clients' data is distributed in non-i.i.d. (non-independent and identically distributed) scenarios. However, due to bandwidth limitation and latency tolerance, the server can only select a subset of clients to participate. Average selection and only selecting low heterogeneous client groups lead to severe results. How to fairly select clients and improve efficient model performance in heterogeneous scenarios with limited communication has become a key issue. We propose a fairness-aware clustered federated learning (FACFL) incentive framework which balances collective and individual fairness. Specifically, our framework models CFL as a bifurcated reverse auction that consists of a first-layer cluster auction and a second-layer client auction. Our framework can dynamically adjust the par-ticipation of clusters and clients according to the communication capabilities. The experimental results on the CIFAR-10 dataset demonstrate that FACFL improves the model performance in severely heterogeneous and communication limited scenarios. Additionally, FACFL can maintain a high level of the training fairness with different numbers of clients.

Flexible manufacturing systems (FMS), empowered by the Industrial Internet of Things (IIoT), have become a cornerstone of Industry 6.0 by enabling dynamic production adaptation, real-time equipment monitoring, and intelligent scheduling. As these systems increasingly incorporate large language models (LLMs) to support functions such as knowledge querying, decision assistance, and predictive maintenance, ensuring the safety and reliability of human-LLM conversations has become a pressing concern. Specifically, LLMs may generate toxic, biased, or privacy-violating outputs when interacting with sensitive IIoT data and production logic, potentially compromising operational safety. To address this challenge, we propose AugLLMSen, an automated toxicity evaluation framework tailored to the IIoT-driven FMS context. AugLLMSen integrates a question automatic expansion mechanism (Q-Judge) and an output toxicity evaluation model (O-Judge) into a closed-loop pipeline, enabling large-scale assessment of LLM safety across diverse industrial scenarios. Experimental results on open- and closed-source LLMs demonstrate the effectiveness and accuracy of our approach in identifying toxic responses and guiding safe deployment of LLMs in flexible manufacturing environments.

Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.

As the large vision language models (LVLMs) and embodied intelligent robotic networks continue to advance at a remarkable pace, particularly in applications spanning smart cities, power grids, factories, and transportation, visual perception and understanding have emerged as foundational elements to overcoming performance limitations in such intelligent systems. However, since general pretrained models are not well-suited to specific tasks, federated fine-tuning (FFT) has gained attention as a promising technique for enhancing the performance of vision-based perception models by leveraging data and computational power distributed across nodes. The rise of advanced persistent threats has revealed significant vulnerabilities in existing defense mechanisms, which struggle to mitigate sophisticated backdoor attacks toward FFT for LVLMs. To address these challenges, this article proposes the SecFFT method, which tackles both the stealthiness and complexity of backdoor strategies. The approach incorporates instantaneous attack behavior detection based on frequency-domain distribution consistency and introduces a long-term secure aggregation mechanism aimed at identifying hidden attack intentions. These strategies effectively limit the feasibility of adversaries attempting to bypass defense measures by concealing their behaviors. Experiments conducted on public datasets demonstrate that SecFFT significantly improves defense success rates, model performance, and detection accuracy, particularly in response to highly covert, multiround backdoor attacks.

With the rapid growth of low earth orbit (LEO) satellites, enabling LEO AI inference becomes a fast-increasing trend. However, due to resource heterogeneity, scheduling complexity, and fast movement, how to decide the place of executing each AI inference task is nontrivial in LEO systems. In this paper, we propose LEOEdge, an edge-assisted AI inference system for LEO satellites. We first introduce the adaptive modeling technologies that automatically generate the model for each satellite according to its computation resources. We then propose a layered scheduling optimization scheme to schedule the AI inference task in a distributed manner. LEOEdge also designs a seamless data transmission scheme to avoid transmission failure due to the LEO satellite movement. We conduct a series of simulation tests to validate the performance of the proposed LEOEdge, in terms of the neural network searching efficiency, average time execution latency, and delivery latency.

Federated learning, as a promising distributed learning paradigm, enables collaborative training of a global model across multiple network edge clients without the need for central data collecting. However, the heterogeneity of edge data distribution drags the model towards the local minima, which can be distant from the global optimum. Such heterogeneity often leads to slow convergence and substantial communication overhead. To address these issues, we propose a novel federated learning framework calledFedCMD, a model decoupling tailored to the Cloud-edge supported federated learning that separates deep neural networks into a body for capturing shared representations in Cloud and a personalized head for migrating data heterogeneity. Our motivation is that, by the deep investigation of the performance of selecting different neural network layers as the personalized head, we found rigidly assigning the last layer as the personalized head in current studies is not always optimal. Instead, it is necessary to dynamically select the personalized layer that maximizes the training performance by taking the representation difference between neighbor layers into account. To find the optimal personalized layer, we utilize the low-dimensional representation of each layer to contrast feature distribution transfer and introduce a Wasserstein-based layer selection method, aimed at identifying the best-match layer for personalization. Additionally, a weighted global aggregation algorithm is proposed based on the selected personalized layer for the practical application ofFedCMD. Extensive experiments on ten benchmarks demonstrate the efficiency and superior performance of our solution compared with nine state-of-the-art solutions. All code and results are available athttps://github.com/elegy112138/FedCMD.

Unmanned aerial vehicles (UAV) are widely used for edge computing in poor infrastructure scenarios due to their deployment flexibility and mobility. In UAV-assisted edge computing systems, multiple UAVs can cooperate with the cloud to provide superior computing capability for diverse innovative services. However, many service-related computational tasks may fail due to the unreliability of UAVs and wireless transmission channels. Diverse solutions were proposed, but most of them employ timedriven strategies which introduce unwanted decision waiting delays. To address this problem, this paper focuses on a taskdriven reliability-aware cooperative offloading problem in UAV-assisted edge-enhanced networks. The issue is formulated as an optimization problem which jointly optimizes UAV trajectories, offloading decisions, and transmission power, aiming to maximize the long-term average task success rate. Considering the discrete-continuous hybrid action space of the problem, a dependenceaware latent-space representation algorithm is proposed to represent discrete-continuous hybrid actions. Furthermore, we design a novel deep reinforcement learning scheme by combining the representation algorithm and a twin delayed deep deterministic policy gradient algorithm. We compared our proposed algorithm with four alternative solutions via simulations and a realistic Kubernetes testbed-based setup. The test results show how our scheme outperforms the other methods, ensuring significant improvements in terms of task success rate.

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%.

Viewport in immersive media corresponds to the field of view (FoV), playing a critical role in both data transmission volume and user experience. However, instantaneous and highly dynamic interactions often conflict with segment-based transmission modes, resulting in substantial redundant data transmission and wastage of valuable resources. In this paper, we analyze data from an open-source dataset and our self-collected records to investigate the interactive characteristics of viewers in immersive scenes, including focus time, viewing area scope, movement direction, and tile access probability. Based on empirical statistical inference, we innovatively introduce the concept of an irregular, expandable, and directional extended field of view (EoV) to describe the dynamically variable area mimicking human visual motion. Furthermore, we propose a motion-aware tile-based adaptive control scheme for viewing areas, named VAAC-IM, designed to enable flexible transmission of immersive media. Specifically, we developed an FoV prediction model based on ConvLSTM, leveraging spatiotemporal features from historical viewing records to provide advanced predictions of visual motion preferences. Subsequently, we model the viewing area control process as a constrained submodular minimization problem, dynamically managing irregular EoV area using marginal effects. 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.

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.

Privacy protection and poisoning resilience are important concerns for federated learning (FL). During a relatively long period, the corresponding solutions are regarded as orthogonal and investigated separately. Unfortunately, due to the increasingly complex structure and ever-growing parameter dimensions of the models to be trained, the forthright coupling of existing differential privacy and Byzantine resilience techniques has been proved incompatible with FL. This emerging problem prompts us to give serious thought to jointly guaranteeing data privacy and model integrity. Besides, worse still, the multi-task characteristic and data imbalance of heterogeneous FL inevitably introduce huge variances, which make privacy-preserving under acceptable accuracy loss even more complicated, not to mention efficient and agile poisoning resistance. Against this bothersome situation, we propose a community-oriented secure heterogeneous FL (CoS-HFL) framework to provide guaranteed privacy protection and significant model robustness simultaneously. CoS-HFL includes two parts: community-oriented duplex privacy amplification and credit-based poisoning resistance. The former copes with potential leakage threats with both uplink and downlink obfuscations. The latter further actively thwarts poisoning attacks based on credibility evaluation. Furthermore, we conduct experiments on benchmark datasets to highlight the performance of CoS-HFL in terms of privacy amplification, poisoning resistance, and learning accuracy under adversarial and heterogeneous environments.

Computation offloading is an effective method for reducing the pressure put on networks and improving the service experience. However, most existing research on computation offloading is timeslot-driven and treats all tasks equally, resulting in decision waiting delays and failure to complete some important tasks. In this paper, we propose a novel priority-aware task-driven computation offloading model with system performance gain as the optimization objective based on a combination of task delay and energy consumption aspects. The new model is formulated as a Markov decision process (MDP). Considering the discrete-continuous hybrid action space of the optimization problem, we construct a dependence-aware latent space and propose a novel algorithm based on the Twin Delayed Deep Deterministic policy gradient algorithm (TD3). Additionally, we present the neural network structure and analyze the complexity of the algorithm. Extensive simulations show how our algorithm achieves superior performance compared to three state-of-the-art alternative approaches.

Recently, the popularity of crowdsourced live streaming (CLS) has increased significantly. To overcome the bandwidth limitations of the broadcaster’s stream in the first mile, some research has introduced video super-resolution (VSR) algorithms at the source server. However, this implementation brings an additional computational burden. In VSR-enabled CLS, the stream push bitrate of the broadcaster will directly determine the volume of data transmitted in the first mile and processed by the source server. An inappropriate upload bitrate can lead to unacceptable delays and resource wastage. To address this issue, this paper proposes a VSR-UAiC framework, which implements an upload adaptive bitrate (ABR) control in VSR-enabled CLS. The VSR-UAiC framework is capable of perceiving multi-dimensional information, including network conditions, computing power, video content, and viewer requests. VSR-UAiC employs an end-to-end reinforcement learning (RL) algorithm to train agents for obtaining the optimal upload ABR strategy. A series of experiments have demonstrated the superiority of the VSR-UAiC framework over alternative solutions in terms of latency, cost, and system capacity.

DDoS attacks pose a fundamental security consideration for any application. With the rapid development of technology, highly QoS-sensitive and rich interaction mobile applications have occupied a great portion of current network traffic. However, owing to their inherent compatibility defects, current shuffle-based solutions fail to accommodate the urgent needs of QoS-sensitive applications, as the defenders require either nonnegligible service interruption or obligatory system modification. Therefore, we propose a feasible DDoS defense solution called RINP, which can be seamlessly integrated with existing applications through its innovative overlay-based wrap mechanism and isolated sidecar implementation. What’s more, we mathematically analyse the communication efficiency and false positive rate of RINP, which provides theoretical foundation for further adaptive defense strategy generation. Last but not least, to demonstrate the superiority of the RINP demo, we conduct a series of experiments in real-world WAN scenarios. The experimental results with Iperf3 (both TCP/UDP modes) show that our compatible defense solution can jointly provide great service continuity and maintain high-quality QoS. To the best of our knowledge, this is the first user-imperceptible and nonintrusive device-cloud collaborative defense solution for QoS-sensitive mobile applications. The source code can be found at https://github.com/bupt-narc/rinp.

Automatic modulation classification (AMC) plays an indispensable role in wireless communication systems. Deep learning-based AMC has become the mainstream solution due to its high accuracy and no need for manual feature engineering. However, every coin has two sides. DL-based AMC is susceptible to adversarial perturbations, which are carefully crafted to be superimposed on the transmitted signals in an iteratively try-and-error manner, resulting in incorrect classification. In this paper, we propose a model diversity-based moving target defense mechanism (MD-MTD), which employs multiple classifiers and switches periodically, preventing intelligent attackers from deducing universal adversarial perturbations (UAP). Besides, to jointly optimize the robustness and accuracy of different AMC models to be trained, we design a novel multi-agent reinforcement learning (MARL) module. It is worth mentioning that the proposed algorithm significantly mitigates the curse of dimensionality during the large-scale training process via integrating value-decomposition networks and illegal action masking, improving the feasibility of our solution in real-world wireless communication systems. Experimental results on the GNU radio dataset also exhibit the remarkable advantages of our method in terms of convergence and defense performance.

The development of LEO satellite networks (LSN) makes them a potential solution to deliver broadcast/multicast traffic to deploy and upgrade massive amounts of Internet of Things (IoT) devices in future 6G networks. However, inherent resource constraints of LSN leave them vulnerable to a multitude of security threats, most notably distributed denial-of-service (DDoS) attacks. Existing solutions are primarily based on machine learning detection methods which are incapable of defending against unknown zero-day attacks. This paper presents an innovative solution leveraging deep reinforcement learning (DRL) to create a dynamic multicast tree based on moving target defense (MTD), aimed at enhancing the security of multicast services in LSN. The proposed solution adopts an adaptive orbital tree mutation (AOTM) scheme that dynamically adjusts multicast tree configurations considering quality of service (QoS) constraints to avoid attacks on vulnerable nodes. Simulations demonstrate the effectiveness of the AOTM scheme, showcasing its superior defense success rates compared to existing state-of-the-art algorithms.

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to web services, making effective defense strategies crucial. However, traditional DDoS protection solutions are often expensive and may not be feasible for all organizations. To address this challenge, this paper presents EclipseFortify, a novel approach that combines the principles of Moving Target Defense (MTD) with cost-effective measures. EclipseFortify leverages the concept of reverse proxies as moving targets, employing proactive shuffling techniques to dynamically allocate valid proxies to users. By disrupting the correlation between users and reverse proxies, EclipseFortify effectively rejects attack traffic, significantly reducing the cost of defense. Furthermore, the method introduces a dynamically updated suspicious rating system to counter advanced attackers who adapt their strategies. To ensure uninterrupted service, EclipseFortify embeds scripts in reverse proxy forwarding, establishing a control link with user-side browsers. This enables seamless switching of reverse proxies without affecting users’ browsing experience. Experimental evaluations demonstrate the effectiveness of EclipseFortify in mitigating DDoS attacks while remaining imperceptible to legitimate users. A demo of EclipseFortify is available at https://hub.docker.com/r/frogsoftware/eclipse-fortify.

Non-terrestrial networks are regarded as crucial infrastructure in forthcoming 6G networks. Edge caching services can be deployed on satellites to optimize the transmission delay of non-terrestrial networks. The dynamically changing user requests and the limited cache space challenge the decision-making process for satellite caching. This paper proposes a Handover-Aware Cache Replacement (HACR) strategy to dynamically replace cache content with satellite mobility. The strategy incorporates the deterministic mobility of satellites to make optimal caching decisions over time and utilizes deep reinforcement learning to implement the strategy. We develop a simulation platform based on the configuration of the Starlink constellation and compare our proposed strategy with conventional methods. The results demonstrate that HACR achieves a 16.74% improvement in cache hit rate and a 48.9 % improvement in stability of cache hit rate compared to the state-of-the-art cache strategy.

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.

As Deep Neural Networks (DNNs) are evolving in complexity to meet the demands of novel applications, a single device becomes insufficient for training, leading to the emergence of distributed DNN training. However, this evolution exposes a gap in research surrounding security vulnerabilities on model poisoning attacks, especially in model parallel setups, an area that has been scarcely studied. To bridge this gap, we introduce Patronus, an approach that counters model poisoning attacks in distributed DNN training, accommodating both data and model parallelism. With the employment of Loss-aware Credit Evaluation, Patronus scores each participating client. Based on the continuously updated credit, malicious clients are isolated and detected after multiple epochs by Shuffling-based Isolation Mechanism. Additionally, the training system is reinforced by Byzantine Fault-tolerant Aggregation to minimize malicious client impacts. Comprehensive experiments confirm Patronus's superior reliable and efficient performance over the existing methods under attack scenarios.

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.

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.

Due to the rapid expansion of the Internet of Vehicles (IoVs), service providers deploy roadside units (RSUs), and base stations (BSs) close to vehicles. They can provide vehicles with computational offloading services quickly. In the context of vehicle social networks, where vehicles can communicate and share data with each other, the security and efficiency of data distribution are crucial. Unfortunately, the open nature of RSU BSs makes them vulnerable to malicious attackers, hence affecting the quality of the user experience. This article proposes a security trust degree incentive-based evaluation mechanism that calculates the security trust degree of vehicle users to RSU BSs through the continuous interaction between them in order to effectively address the aforementioned issues. Additionally, taking into account the competitive nature of task computation offloading between vehicle users and BSs, a stable matching algorithm is used to match each vehicle user with the most appropriate BS so that they can work together to prevent competition in task offloading and improve task offloading efficiency. Due to the limited number of BS matches and the dynamic position changes of vehicle users, we further increase the data distribution efficiency by calculating the vehicle user degree of relationship and connection probability to match vehicle users with similar preferences. Finally, our proposed scheme is validated via numerous simulations with enhanced security service performance in terms of vehicle task offloading, while data distribution efficiency are effectively improved.

Mobile edge computing (MEC) has emerged as a solution to address the demands of computation-intensive network services by providing computational capabilities at the network edge, thus reducing service delays. Due to the flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed to assist MEC. This paper investigates the task offloading problem in a UAV-assisted MEC system with collaboration of multiple UAVs, highlighting task priorities and binary offloading mode. We defined the system gain based on energy consumption and task delay. The joint optimization of UAVs' trajectory design, binary offloading decision, computation resources allocation, and communication resources management is formulated as a mixed integer programming problem with the goal of maximizing the long-term average system gain. Considering the discrete-continuous hybrid action space of this problem, we propose a novel deep reinforcement learning (DRL) algorithm based on the latent space to solve it. The evaluation results demonstrate that our proposed algorithm outperforms three state-of-the-art alternative solutions in terms of task delay and system gain.

The exceptionally high bandwidth requirement for delivering high-quality live 360$^\circ$video poses a significant challenge to current network capacity. Mitigating such bandwidth starvation necessitates accurate field-of-view (FoV) prediction to focus limited resources on the viewer's area of interest. However, FoV prediction for live 360$^\circ$streaming can be complex due to the time-sensitive nature of live content and the limited knowledge available for model training. Our paper introduces a novel framework,CoLive, for predicting the FoV in 360$^\circ$live streaming.CoLiveaccelerates FoV prediction by offloading model training from viewers to the edge and migrating saliency feature detection to the server side. Observations on user clustering of viewing behaviors further motivate us to propose a novel dynamic clustered learning algorithm. The algorithm dynamically groups users according to their model update gradients and enables them to train a shared model that better suits their viewing preferences. We conduct extensive experiments on the public 360$^\circ$video datasets and demonstrate thatCoLiveoutperforms state-of-the-art solutions in terms of prediction performance and bandwidth savings.

5G vehicular networks provide abundant multimedia services among mobile vehicles. However, due to the mobility of vehicles, large-scale mobile traffic poses a challenge to the core network load and transmission latency. It is difficult for existing solutions to guarantee the quality of service (QoS) of vehicular networks. Besides, the sensitivity of vehicle trajectories also brings privacy concerns in vehicular networks. To address these problems, we propose a privacy-preserving and trajectory clustering-based framework for cooperative caching optimization (PTCC) in vehicular networks, which includes two tasks. Specifically, in the first task, we first apply differential privacy technologies to add noise to vehicle trajectories. In addition, a data aggregation model is provided to make the trade-off between aggregation accuracy and privacy protection. In order to analyze similar behavioral vehicles, trajectory clustering is then achieved by utilizing machine learning algorithms. In the second task, we construct a cooperative caching objective function with the transmission latency. Afterwards, the multi-agent deep Q network (MADQN) is leveraged to obtain the goal of caching optimization, which can achieve low delay. Finally, extensive simulation results verify that our framework respectively improves the QoS up to$9.8\%$and$12.8\%$with different file numbers and caching capacities, compared with other state-of-the-art solutions.

Recently, blockchain-based covert communication has gained momentum, for the decentralization, anonymity, and immutability feature of blockchain. Nevertheless, some challenges impair its security and efficiency. Most schemes have a weak generalization ability, and can merely be applied to a specific blockchain platform. Storage-based covert transmission schemes usually have limited space for data embedding, affecting their Information delivery efficiency. Besides, static data sifting rules raise the risk of information leakage. In this paper, we design DLCCB(Dynamic Labeling based Covert Communication on Blockchain). We first split the information to be delivered into several pieces and utilize the destination address of each transaction to embed them. Then a dynamic labeling method is proposed for updating sifting rules without extra negotiation between sender and receiver. Besides, we design two kinds of sifting algorithms, namely online and offline sifting algorithm. We perform our solution on Ropsten, a test net of Ethereum. The experiment result verifies the feasibility of our scheme.

Field of view (FoV) prediction is a crucial issue in 360° video streaming, which is the basis for selectively transmitting panoramic videos to reduce bandwidth. The saliency feature is a very important part of FoV prediction. The saliency area identifies a user’s region of interest (RoI) and reflects the user’s viewing behavior preference. The regular convolutional neural network (CNN) cannot effectively extract the spatial representation of panoramic video content because significant geometric distortion will be introduced after panoramic video projection, especially in polar regions. In this paper, we propose a depth neural network model based on spherical convolution, which can learn the spatial features of the 360° videos by encoding the distortion invariance into the architecture of CNNs. A series of experiments on the public 360° video saliency dataset show the proposed model outperforms the existing saliency models. Finally, we embed the proposed saliency network into a popular FoV prediction framework and propose a complete FoV prediction framework for 360° video streaming.

With rapid development of emerging technologies for Internet of Things (IoT), digital twins (DT) have been proposed to support a wide variety of applications. A mobile network is expected to be integrated with DT to form a DT mobile network (DTMN). Unfortunately, DTMN still faces security threats, which have attracted great research attention. Current defense mechanisms are mostly static, i.e., responding after attacks happening. To solve the aforementioned problem, moving target defense (MTD) has been proposed as an innovative solution. However, there exist three major challenges when applying MTD into DTMN. Firstly, less emphasis was paid to collaborative scheduling between multiple MTD schemes, which can improve the security of DTMN. Secondly, MTD schemes require lots of network resources, but few works focus on the time allocation of multiple MTD schemes to reduce network resource consumption. Thirdly, existing defense strategies only rely on current information, but do not consider future information. In this paper, we propose a collaborative mutation-based MTD (CM-MTD) in DTMN. We mainly consider two MTD schemes called host address mutation (HAM) and route mutation (RM), respectively, which adjust network properties and invalidate different stages of cyber kill chain. We firstly formulate a semi-Markov decision process (SMDP) to model time-varying security events and dynamic deployment of multiple MTD schemes. Then, security events are predicted by long short-term memory (LSTM), which are regarded as network states in SMDP. Next, infeasible actions that do not satisfy network constraints will be removed from the action space of the SMDP. Lastly, we design a hierarchical deep reinforcement learning algorithm for collaborative scheduling. Simulation results highlight the effectiveness of CM-MTD compared with baseline solutions.

Kubernetes (K8s) has become a core technology for cloud-native applications. However, a design flaw of the external IP in K8s leads to the service-oriented man-in-the-middle attack. Existing solutions (e.g., script monitor) attempt to address it passively, which allows attackers enough analysis time to bypass these static rule reviews. Differently, we propose a mutation-enabled proactive defense mechanism, aiming to change the asymmetry between attackers and defenders. It involves the address mutation (i.e., network identification) module and the connection ID (i.e., communication identification) mutation module. In the former module, we analyze mutation constraints and prove the corresponding mutation grouping problem to be NP-hard. Then, a maximally coloring-driven mutation grouping algorithm is developed. Since the address allocation time grows linearly with the service size, we design a prefetched address allocation algorithm. After designing the interaction flow between modules, we present a randomized algorithm in the latter module. Thus our mechanism does not affect methods oriented to other attacks. Eventually, it can continuously interrupt the attack and keep the service connection by incrementally updating K8s and the transport layer protocol. Experiments in the Alibaba cloud demonstrate that it can effectively defend against the attack with an acceptable performance loss.

Edge computing can reduce service latency and save backhaul bandwidth by completing services at network edges, providing support for diverse computation-intensive and delay-sensitive services. However, it is not practical to support all services at edge nodes due to the limited network resources. The decision that which services can be provided locally and which services should been offloaded to cloud significantly impacts the user experience. Cloud-edge computing offloading becomes an important issue in edge computing. In this paper, we take the fairness into the optimization objective of computing offloading problem, and consider both computing capacity and storage space as problem constraints. The problem is formulated as a long-term average optimization problem to maximize the α-fair utility function of saved time, and further translated as a Markov decision process. As the optimization problem with fairness guarantee and huge action space, we cannot solve it with traditional methods. Therefore, an innovative multi-update deep reinforcement learning algorithm is proposed which can optimize the objective with α-fair utility function and reduce dramatically the size of action space. We also prove the convergence of our algorithm theoretically. To our best knowledge, the long-term average optimization of computing offloading with fairness guarantee is rarely seen in literature. Extensive simulation experiments show that our algorithm can converge quickly and has better performance in terms of service delay and fairness.

Network function virtualization (NFV) supports the rapid development of service function chain (SFC), which efficiently connects a sequence of network virtual function instances (VNFIs) placed into physical infrastructures. Current SFC migration mechanisms usually keep static SFC deployment after finishing certain objectives, and deployment methods mostly provide static resource allocation for VNFIs. Therefore, the adversary has enough time to plan for devastating attacks for in-service SFCs. Fortunately, moving target defense (MTD) was proposed as a game-changing solution to dynamically adjust network configurations. However, existing MTD methods mostly depend on attack-defense models, and lack adaptive mutation period. In this article, we propose an Intelligence-Driven Service Function Chain Migration (ID-SFCM) scheme. First, we model a Markov decision process (MDP) to formulate the dynamic arrival or departure of SFCs. To remove infeasible actions from the action space of MDP, we formalize the SFC deployment as a constrained satisfaction problem. Then, we design a deep reinforcement learning (DRL) algorithm named model-based adaptive proximal policy optimization (MA-PPO) to enable attack-resistant migration decisions and adaptive migration period. Finally, we evaluate the defense performance by multiple attack strategies and two realistic datasets called CICIDS-2017 and LYCOS-IDS2017 respectively. Simulation results highlight the effectiveness of ID-SFCM compared with representative solutions.

Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals’ wish to “hide in the crowd” for privacy frequently clashes with aggregators’ need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.

With the explosive growth of Internet traffic, large sensitive and valuable information is at risk of cyber attacks, which are mostly preceded by network reconnaissance. A moving target defense technique called host address mutation (HAM) helps facing network reconnaissance. However, there still exist several fundamental problems in HAM: 1) current approaches cannot be self-adaptive to adversarial strategies; 2) network state is time-varying because each host decides whether to mutate IP address; and 3) most methods mainly focus on enhancing security, but ignore the survivability of existing connections. In this paper, an Intelligence-Driven Host Address Mutation (ID-HAM) scheme is proposed to address aforementioned challenges. We firstly model a Markov decision process (MDP) to describe the mutation process, and design a seamless mutation mechanism. Secondly, to remove infeasible actions from the action space of MDP, we formulate address-to-host assignments as a constrained satisfaction problem. Thirdly, we design an advantage actor-critic algorithm for HAM, which aims to learn from scanning behaviors. Finally, security analysis and extensive simulations highlight the effectiveness of ID-HAM. Compared with state-of-the-art solutions, ID-HAM can decrease maximum 25% times of scanning hits while only influencing communication slightly. We also implemented a proof-of-concept prototype system to conduct experiments with multiple scanning tools.

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.

The software-defined wide area network (SD-WAN) is considered one of the most promising paradigms for the next generation enterprise networks. However, SD-WAN users usually suffer from significant propagation delays due to the remotely deployed cloud centers. The requirements of delay-sensitive business services make the use of fog nodes and optimal resource allocation methods very important. In this paper, we propose a revenue-oriented service offloading method to improve the efficiency of SD-WAN through fog-cloud collaboration. Aiming at maximizing the service revenue, we formulate a coupled combinatorial optimization model to jointly allocate computation and communication resources in both the fog node and the cloud. To solve this problem, we propose a service offloading decision-making method based on the counterfactual regret minimization (CFR) principle according to the workload state of the fog node. This method reduces the time complexity of solving the original problem from exponential to polynomial by providing an approximate optimal solution, and achieves a good performance that is very close to the optimal solution in terms of service efficiency. Simulation results show that our method outperforms benchmark approaches in terms of both effectiveness and efficiency.

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.

The ever-increasing demand for bandwidth resources when delivering premium quality 360° video challenges the current network capacity. To alleviate such bandwidth pressure, it is imperative to predict the viewport via observing the content visual feature and historical viewing behaviors, which thereby allows the system to concentrate the limited resource on viewer's region of interest in 360° content. However, enabling accurate viewport prediction for 360° live streaming is non-trivial given the time-sensitive of live content and shortage of pre-knowledge on the visual features and viewing behaviors. In this paper, we propose CoLive, an edge-assisted online viewport prediction framework. CoLive incorporates edge computing to offload the prediction model training from viewers and migrates the saliency feature detection to the server side for reducing the processing delay. Viewers can also collaboratively train a central predicting model via sharing their loss gradients. This central model, together with the saliency feature detection, further prompts accuracy prediction and learning acceleration, especially for new incoming viewers. A series of experiments on the public 360° video dataset show how our solution achieves better performance compared with state-of-the-art solutions.

Network performance of multicast transmissions such as bandwidth, utilization and delay is very important to improve the quality of user experience and the above constraints have to be considered. Most of the current multicast protocol infrastructures use static routes, however, static routing policies provide potential convenience for attackers who can perform network sniffing or initiate DoS attacks. We propose a multi-constraint multicast routing mutation mechanism to enable dynamic changes in multicast routing while satisfying certain quality of service to achieve proactive multicast routing defense, which increases the attacker's attack cost and reduces the defender's defense overhead. In addition, for the high time complexity of the multicast tree generation algorithm, we propose a multi-controller multicast routing algorithm with multi-domains. We perform simulations and show that the defense performance of multicast routing is significantly improved after adopting this mechanism.

Bitcoin and Ethereum have always been the two major heavyweight infrastructures in the blockchain space. However, Low throughput and high cost hinder their further development. Recently, some emerging public blockchains have become popular. They all have efficient transaction confirmation mechanism and cheap interaction costs. However, the advantages are actually a sacrifice of decentralization. As we all know, decentralization is an essential feature of blockchain. Therefore, it requires the conceiving of up-to-date metrics of decentralization measurement. However, there is little research on the degree of decentralization of these emerging public blockchains in the past. This paper studies nine popular public chains such as Binance Smart Chain, Cardano, and Avalanche. Since these public chains mostly use the consensus mechanism of POS variants, the distribution of governance token balances on the chain can reflect the decentralization of the blockchain. Hence, We evaluate the distribution of the token balance of those public blockchains to indicate their decentralization degree. Two kinds of indicators are adopted and redesigned: information entropy and Gini coefficients. Among the nine public blockchains we selected, Cardano, Tron and Polkadot have a higher degree of decentralization, while Elrond and Binance Smart Chain have a lower degree of decentralization. We think our work will be helpful for future research on the degree of blockchain decentralization.

Concurrent multipath transfer(CMT) can make better use of network resources to increase the data transmission rate. However, there are heterogeneous paths in the real network, the existing scheduling strategy is fixed and does not have self-adaptation, there are still some problems with delay and throughput performance, which reduces the reliability of transmission. Therefore, we propose a novel CMT scheduling strategy based on multi-QoS (CMT-MQ). Combined with reinforcement learning (RL), scheduling strategies are generated according to service QoS requirements and path characteristics. At the same time, in order to reduce the action space of RL and eliminate poor paths, clustering algorithm is introduced to filter the set of paths before training. Finally, the simulation comparison experiment on OmNET++ shows that CMT-MQ has higher throughput and lower message delay.

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

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.

Vehicular Ad hoc Networks (VANETs) are prone to packet drop attacks because of their inherent distributed architecture and dynamic topology. Existing security schemes mainly focus on multi-path and trust-based routing. Unfortunately, the former causes high energy consumption and the latter requires trust assessment, which is not easy to implement in practice. Route mutation (RM) is emerging as an active defense technology that changes routes periodically. Traditional RM is conceived for fixed network topologies, and needs a centralized controller, so that it cannot be applied to VANETs. Therefore, the present contribution investigates RM in VANETs by proposing a Grid-based extended Joint Action Learning approach (Grid-eJAL). To the best of our knowledge, this is the first contribution that designs an online and adaptive multi-agent reinforcement learning (MARL) for RM to mitigate attacks in VANETs. Differently from existing MARL schemes, Grid-eJAL allows vehicles to share parameters to accelerate the convergence speed of learning. In Grid-eJAL, the area of interest is split in equally sized grids and, when a vehicle transmits packets, the next hop with the minimum angle of mobility is selected within the grid considered as optimal by the learning policy. The convergence of Grid-eJAL is proved theoretically. Finally, extensive simulation results highlight the effectiveness of Grid-eJAL compared to representative state-of-the-art solutions.

Multi-path transmission control protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose theReinforcementLearning-based mobile AR/VR multipath transmission with streamingPowerSpectrumDensity analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams.

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.

In today's “Internet of Everything (IoE)” era, the collaboration from massive participants significantly boosts the performance and efficiency of model training. This trend also un-avoidably stirs up considerable concerns about multi-dimensional security problems. Under the circumstances, federated learning (FL) is enthusiastically adopted, as it protects privacy to a certain extent by only processing personal data locally. Nevertheless, FL's characteristics of concealment also pave the way for sev-eral emerging attacks during the training process, i.e., model inversion, poisoning, and backdoor. Currently, although partially mitigating attack effects, existing countermeasures against those threats are studied separately and orthogonal. This separation makes those defense methods mutually exclusive and restrictive in real-world application scenarios, far from satisfying. In this paper, we extensively model different attack paradigms into three types based on CIA-triad, the well-known information security primitive, and propose a novel dual-shuffle method to thwart aforementioned threats jointly. Concretely speaking, our primary model shuffling mechanism provides the confidentiality guarantee based on the information-theoretic notion of identifiability; then, an augmented client shuffling mechanism purges the user group of adversaries proactively without any compromise of anonymous constraints. By conducting a series of experiments on bench-mark datasets, we demonstrate that our method could achieve significant security and convergence performance against three state-of-the-art attacks.

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.

Software-defined wide area network (SD-WAN) is a new type of network architecture that has developed rapidly in recent years. SD-WAN inherits the centralized control ar-chitecture of Software-defined networking (SDN), but supports more diverse access methods and equipment types and covers a wider area. It is also associated with greater uncertainty in the network environment. These characteristics make the fault management of the SD-WAN controller more challenging, so that the existing SDN-based data backup methods cannot adapt to SD-WAN scenarios. This paper proposes an SD-WAN-oriented Distributed Data Backup and Recovery method (DDBR) based on an improved secret sharing algorithm. To deploy this method, we design an online-offline dual backup framework based on the data freshness requirements of the controller. Under this framework, dynamic data of the controller is divided into different shares, and then stored into the storage of switches. When the controller fails, data recovery can be performed on the backup controller quickly, which greatly improves the network availability. The outstanding feature of the proposed DDBR method is that it ensures the integrity and confidentiality of the backup data in an unreliable network environment, even when some of the storage nodes fail. Evaluation results on file backup example show that the proposed solution has significant advantages over existing methods in terms of backup data storage size and backup success rate.

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.

The growing multimedia services have brought unprecedented challenges to the traditional static network architecture. Moving Target Defense (MTD) has been proposed to solve the inherent disadvantages of existing defense techniques. As an important area of MTD research, Route Mutation (RM) can dynamically change the forwarding routes in the network. In our previous work, we applied Reinforcement Learning (RL) to RM. However, there are still two problems that need to be addressed. 1) We consider too few constraints to reflect the actual network situation. 2) Due to the slow rate of convergence, it becomes difficult for efficient deployment. In this paper, we propose a Proximal Policy Optimization Based Route Mutation (PPO-RM) scheme to solve these problems. Firstly, we utilize the Satisfiability Module Theory (SMT) to formalize the space of all possible mutated routes. Then, we design an RM algorithm based on Proximal Policy Optimization (PPO) and implement it in the SDN controller. Finally, we simulate on Mininet to validate our method. The experiment results show that PPO-RM achieves the improvement in terms of convergence rate, defense performance, and network performance.

Moving target defense (MTD) is an emerging proactive defense technology, which can reduce the risk of vulnerabilities exploited by attacker. As a crucial component of MTD, route mutation (RM) faces a few fundamental problems defending against sophisticated Distributed-Denial of Service (DDoS) attacks: 1) it is unable to make optimal mutation selection due to insufficient learning in attack behaviors and 2) because network situation is time varying, RM also lacks self-adaptation in mutation parameters. In this article, we propose a context-aware Q-learning algorithm for RM (CQ-RM) that can learn attack strategies to optimize the selection of mutated routes. We first integrate four representative attack strategies into a unified mathematical model and formalize multiple network constraints. Then, taking above network constraints into considerations, we model RM process as a Markov decision process (MDP). To look for the optimal policy of MDP, we develop a context estimation mechanism and further propose the CQ-RM scheme, which can adjust learning rate and mutation period adaptively. Correspondingly, the optimal convergence of CQ-RM is proved theoretically. Finally, extensive experimental results highlight the effectiveness of our method compared to representative 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.

Community detection for Location-based Social Networks (LBSNs) has been received great attention mainly in the field of large-scale Wireless Communication Networks. In this paper, we present a Differential Privacy Latent Trajectory cOmmunity Discovering (DP-LTOD) scheme, which obfuscates original trajectory sequences into differential privacy-guaranteed trajectory sequences for trajectory privacy-preserving, and discovers latent trajectory communities through clustering the uploaded trajectory sequences. Different with traditional trajectory privacy-preserving methods, we first partition original trajectory sequence into different segments. Then, the suitable locations and segments are selected to constitute obfuscated trajectory sequence. Specifically, we formulate the trajectory obfuscation problem to select an optimal trajectory sequence which has the smallest difference with original trajectory sequence. In order to prevent privacy leakage, we add Laplace noise and exponential noise to the outputs during the stages of location obfuscation matrix generation and trajectory sequence function generation, respectively. Through formal privacy analysis, we prove that DP-LTOD scheme can guarantee ϵ-differential private. Moreover, we develop a trajectory clustering algorithm to classify the trajectories into different kinds of clusters according to semantic distance and geographical distance. Extensive experiments on two real-world datasets illustrate that our DP-LTOD scheme can not only discover latent trajectory communities, but also protect user privacy from leaking.

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.

As massive research efforts are poured into server-side DNS security enhancement in online cloud service platforms, sophisticated APTs tend to develop client-side DNS attacks, where defenders only have limited resources and abilities. The collaborative DNS attack is a representative newest client-side paradigm to stealthily undermine user cache by falsifying DNS responses. Different from existing static methods, in this paper, we propose a moving target defense solution named multi-vNIC intelligent mutation to free defenders from arduous work and thwart elusive client-side DNS attack in the meantime. Multiple virtual network interface cards are created and switched in a mutating manner. Thus attackers have to blindly guess the actual NIC with a high risk of exposure. Firstly, we construct a dynamic game-theoretic model to capture the main characteristics of both attacker and defender. Secondly, a reinforcement learning mechanism is developed to generate adaptive optimal defense strategy. Experiment results also highlight the security performance of our defense method compared to several state-of-the-art technologies.

The development of vehicular networks has greatly improved the efficiency and safety of intelligent traffic systems. However, it also introduces additional security threats into the system. The special characteristics of vehicular networks, such as dynamic topology, huge network scale and so on, make it difficult to adopt the traditional security solutions directly into this scenario. In addition, the single point failure problem of the centralized security mechanisms is also a big challenge. Recently, blockchain technology, which is a distributed database, is a potential approach to address these security issues. In this paper, a comprehensive review of the existing blockchain-based cybersecurity mechanisms is presented with the corresponding performance analysis. The purpose of this work is to provide a guideline for the further study in the application of blockchain in the vehicular network security area.

Increasingly growing various multimedia services (e.g., interactive live video and so on) have brought tremendous pressure on existing static defense techniques. To cope with inherent drawback of static defense techniques, Network Moving Target Defense (NMTD) such as route mutation (RM) was proposed. What's more, applying reinforcement learning (RL) into RM has been proved feasible in our previous work. But two main problems still need to be considered in this combination of RL with RM: 1) It lacks the consideration of multiple flows situation. 2) With the state-action space grow larger, current solution can't handle efficiently. In this paper, we propose a deep Q-learning method for RM (DQ-RM) to solve above two problems. Firstly, benefited from the satisfiability module theory, we formalize RM space considering single flow and multiple flows concurrently. Then we further propose a deep reinforcement learning-based RM scheme based on our previous work, which is suitable for large-scale state-action space. Finally, extensive experimental results highlight the improvement of DQ-RM in defense performance and convergence speed compared to the representative solution.

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.

Multipath TCP (MPTCP) has recently been suggested as a promising transport protocol to boost the utilization of underlaying datacenter networks, yet it also increases the host CPU power consumption. It remains unclear whether datacenters can indeed benefit from using MPTCP from the perspective of energy efficiency. Through realworld measurement of MPTCP, we show that the energy efficiency of MPTCP is largely related to the flow completion time and the existence of link-sharing subflows. In particular, we find that the link-sharing subflows in MPTCP will significantly elevate the CPUs' power consumption on hosts. To make the matter worse, it will also reduce the transmission efficiency for both throughput-sensitive long flows and latency-sensitive short flows. To address such a problem, we present MPTCP-D, an energy-efficient enhancement of MPTCP in datacenter networks. MPTCP-D incorporates a novel congestion control algorithm that improves energy efficiency by minimizing the flow completion time. It also has a build-in subflow elimination mechanism that precludes link-sharing subflows from increasing the host CPU power consumption. We implement MPTCP-D in the Linux kernel, analyze the parameter selection in the algorithm and study its performance through packet-level simulation and on Amazon EC2. Our results show that, without degrading the performance of the long flow throughput and the short flow completion time, MPTCP-D reduces the long flow energy consumption by up to 72% compared to DCTCP for data transfers, and reduces the short flow power consumption by up to 46% compared to MPTCP with link-sharing subflows.

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.

Static network defense technologies are always in a passive defense state because of their disadvantages in cost, time and information. So Network Moving Target Defense (NMTD) is proposed as a kind of proactive defense technology. As an important research direction of NMTD, route mutation techniques still have limitations that they can not learn attack strategies and be adaptive in dynamical security situation. In this paper, we propose a novel route mutation mechanism based on reinforcement learning. We firstly investigate four different attack strategies and introduce a mixed attack strategy with entropy constraints. Then we formulate the network requirements using Satisfiability Module Theory (SMT) logic to acquire the route mutation space. We further propose a security-awareness Q- learning algorithm to select routes from the mutation space iteratively and conduct security awareness to adjust learning rate adaptively. Meanwhile, the optimal convergence of our algorithm is proved theoretically. Finally, experimental results highlight the effectiveness as defense performance, network overhead and convergence speed of our method compared to the representative solution.

In vehicular ad-hoc network (VANET), vehicles exchange information on road conditions which guarantees safety. However, there exists malicious nodes which interfere the communication between vehicles. Thus, it is vital to identify malicious vehicles in VANET. Reputation-based schemes are one of the most promising schemes to identify malicious nodes in time. However, existing methods can only identify malicious nodes of a specific attack. Additionally, the efficiency and effectiveness of existing work in solving advanced attacks are unsatisfactory. In this paper, we propose a scheme to identify malicious nodes in VANET based on collaborative filtration. Distinguished from the existing solutions, we consider a variety of attacks in VANET, instead of a specific attack. In addition, our scheme updates the reputation of nodes in time according to the result of each communication, which brings better efficiency and effectiveness. The superiority of our proposed scheme has been demonstrated through simulation experiments.

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.

For the reasons that defect remedy is an endless arduous work for static network defense technologies and cyberspace security remains unguaranteed, moving target defense (MTD) is proposed to stem the tide. Whereas, as an important branch of MTD, route mutation technologies still have limitations against some sophisticated adversaries like Advanced Persistent Threat (APT), multiple-step complex or combined attacks. In this paper, we propose a new spatio-temporal route mutation method based on MTD. We first take the maximization of resistibility towards not only multiple forms of attacks but also attackers' long-term background knowledge into consideration. We also formulate the problem into a stochastic optimization model and make it possible to agilely generate the satisfying mutation route meets the demands of various parties jointly by only solving one uniform problem. Thus, network Security is guaranteed from both flows(users) and nodes(infrastructure) perspectives. Experimental results highlight the security advantages as traffic dispersion, potential victim number and attack failure rates of our method compared to existing solutions.

By moving computation and caching to the network edge, Mobile Edge Computing (MEC) offloads core networks and shortens data access latencies, which is important for large scale mobile multimedia services. Increasing the density of edge data centers to service these multimedia requests is uneconomical. Recent research has proven the benefits of involving devices in the delivery of multimedia services. This is done by exploiting the idle computation and storage resources via device-to-device (D2D) communication, i.e., by forming a so-called Mobile Device Cloud (MDC). Despite the flexibility and cost efficiency of this MDC paradigm, the timely allocation of caching resources to satisfy the dynamic user demands is challenging. This is mainly due to the uncertainty in resource availability of mobile devices. To this end, we propose Edge-Boost, a novel MDC caching architecture for lowlatency multimedia streaming services. We develop a novel fluid-based model to capture the dynamically changing network status. Additionally, we propose a dynamic caching allocation to jointly minimize caching cost and service latency. Edge-Boost achieves over 20% higher average cache utilization and 15% shorter average access latency than the state-of-the-art MDC approach.

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.

With the explosion of smart devices and the gradual maturation of mobile systems, mobile crowdsensing (MCS) is playing more and more important roles in our daily life. In traditional MCS with a centralized framework, participants directly send perceived information to the task provider alone. This framework greatly increases the burden of cloud-based servers and cannot make full use of the increasing computation and storage capabilities of Internet of Things devices. To offload the computing and storage burden from traditional MCS architecture, a distributed MCS architecture was proposed in this paper, in which participants exchange sensing information with each other rather than forward it to central servers to complete a task together. Then, a diffusion Kalman filtering algorithm with quantized information exchange (QDKF) was proposed to solve the dynamic real-time estimate problem and limited communication resources in distributed MCS, where nodes exchange their quantized observations with neighbors to reduce the consumption of computing and storage resources. To prove the convergence and stability of the QDKF algorithm, an in-depth analysis of the algorithm uncertainty was reported to completely characterize the proposed solution. Moreover, the proposed algorithm achieves a superior performance by simulation.

The latest increasing popularity of high-speed railways (HSR) has stimulated growing demands for wireless Internet services in HSR networks, especially for video streaming. However, due to the high variability and unpredictability of wireless communications in HSR networks, it is still difficult for the existing solutions to provide high-quality video streaming services to HSR passengers. This paper addresses this crucial problem first by reporting on field experiments performed to investigate the characteristics of HSR networks. Then the paper formulates an intractable optimization problem for dynamic adaptive streaming over HTTP (DASH)-enabling service in HSR networks considering various factors, including packet loss, energy consumption, video service quality, etc. By leveraging Lyapunov optimization approaches, the formulated optimization problem is transformed into a queue stability problem which is of high scalability and generality. Moreover, in order to overcome the intractability of the initial optimization problem, the queue stability problem is further decomposed into three subproblems which can be easily solved individually. Finally, a novel joint stochastic DASH optimization (JSDO) mechanism consisting of three algorithms for the derived subproblems is proposed. Rigorous theoretical analyses and realistic dataset-based simulations demonstrate the effectiveness of the proposed JSDO mechanism.

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.

The recent proliferation of public hotspots has given rise to Wi-Fi advertising where venue owners promote their business by pushing advertisers' advertisements on their hotspots. However, a small business usually has insufficient budget to make a purchase for a whole webpage. Therefore, in this paper, we propose GAWA, a Group-buying based Auction mechanism for Wi-Fi Advertising among a venue owner, group leaders and advertisers, which is composed of three phases. More specifically, in the first phase, we propose an algorithm to decide a group bid for each group leader and winning advertisers for each group. In the second phase, the venue owner assigns venues to group leaders by a novel winning group leader determination algorithm. In the third phase, the mechanism determines how much each winning group leader should charge each advertiser in the winning group. We prove that GAWA is computationally efficient, and possesses excellent economic properties such as individual rationality, budget balance, and truthfulness. We evaluate the proposed algorithms using large-scale simulations, and demonstrate the effectiveness and efficiency of our design when comparing with the state-of-the-art approaches.

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.

Information-centric networking (ICN), as a novel network paradigm, is expected to natively support mobility, multicast, and multihoming in vehicular ad hoc networks (VANETs). In this paper, the adoption of ICN principles for multimedia streaming in multihomed VANETs is investigated, with a major emphasis on the tradeoff between the quality of experience and energy efficiency (EnE). To formalize this problem, a cost optimization model is first proposed, based on queueing theory arguments. Then, a novel green information-centric multimedia streaming (GrIMS) framework is designed to drive the system toward optimal working points in practical settings. GrIMS consists of three enhanced mechanisms for on-demand cloud-based processing, adaptive multipath transmission, and cooperative in-network caching. Finally, a massive simulation campaign has been carried out, demonstrating that, thanks to its core components, the GrIMS enables flexible multimedia service provisioning and achieves an improved performance in terms of start-up delay, playbacks continuity, and EnE with respect to state-of-the-art solutions.

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.

Widespread deployment of High-Speed Railway (HSR) in recent years brings strong demand for high-quality onboard Internet services. However, the wireless link between the train and Base Station (BS) suffers from numerous problems, including frequent handover, severe Doppler shift, and great penetration loss, etc. It is still a challenge to provide HSR passengers with high-quality Internet services. In this paper, the Packet Loss Rate (PLR) of HSR networks is measured along Beijing-Shanghai railway line. Measurement results indicate that PLR remains at a high level for a long period and changes frequently in a large interval. To address this problem, we focus on frame loss problem of train-to-BS wireless link and propose a novel Loss-Aware Adaptive Scalable Transmission mechanism (LAAST) to fit for HSR networks. In LAAST, variable number of frame copies are transmitted according to Frame Loss Probability (FLP) to improve the scalability and efficiency of transmission over train-to-BS link. The optimal relationship between frame duplication number and FLP is derived through nonlinear programming model. Simulations demonstrate the effectiveness and fitness of LAAST for HSR networks.

Information-Centric Networking (ICN) focuses on data dissemination instead of host communication, and it is a promising solution for video streaming in Vehicular Ad-hoc Networks (VANETs). In Information-Based VANETs, content copies can be cached by arbitrary mobile nodes in the networks. Thus, it is required to optimize the routing of content requests (referred to as Interest packets) in order to quickly locate potential content providers as soon as possible. This article proposes a Preference-aware Fast Interest Forwarding for video streaming in ICN-based VANETs (PaFF). In PaFF, each mobile user creates a Highly Preferred Content Table (HPCT) to maintain the content caching status of nodes who have similar mobility patterns and video playback behavior. Based on HPCT, a preference-aware forwarder selection mechanism is proposed to select the next hop of Interest packets in order to minimize latencies and maximize reliability. Simulation results show that PaFF achieves a considerable improvement in terms of start-up delay and cache hit ratio while incurring almost the same overhead with respect to state-of-art solutions.

Cognitive radio networks enable the unlicensed users to share the spectrum with licensed users, while it makes dynamic environment. The use of the channel by unlicensed users should be sensible of the licensed users in advance whether acceptable interference occurs. Mostly the routing protocol considered the previous state prediction. However, the routing probe and react strategy by unlicensed users sometimes is not only determined by the last one node state. In this paper we introduce a predictive node mobility model which is monitoring a licensed user's mobility, capable of reducing the delay.

This paper proposed a mobility-aware multimedia data transfer mechanism using Multipath TCP in Vehicular Network. Since high transmission rate and low latency are the two key factors for multimedia data transmission that could provide stable video streaming services, therefore, we first adopted Multipath Transport Control Protocol which can transfer data concurrently for improving transmission rate and designed Quality-aware Data Distribution to dynamically allocate the data to different subflows. Moreover, in Vehicular Network, the mobile terminal, which means the vehicle, can communicate with remote server through roadside unit (RSU). However, the communication link between terminals and remote server will disrupt while the vehicle exceeding the communication range of roadside unit; and there also exists the situation that the vehicle is in the communication range of several RSU. Accordingly, we exploited a mobility-aware distance measurement for checking whether the vehicle has moved out of the communication range of any RSU or it is in multi-RSU's communication range. Afterwards, we designed a handover mechanism which transfer data to connected path (4G) for stable transmission using MPTCP when the vehicle exceeded the communication range of RSU; while one mobile can communicate with more than one RSU, we exploited a mechanism which can trigger new path for multipath data transmission and non-corporation Nash Equilibria was employed for solving the problem of fairness in the same kind of network technology multipath transmission. Simulation results show how mobility-aware multimedia data transfer mechanism improve the performance of transmission comparison with state-of-art solution.

We study the distributed Kalman filter in sensor networks where multiple sensors collaborate to achieve a common objective. Diffusion Kalman filtering algorithms have been a popular topic in linear dynamic system estimation problems, In there algorithms, nodes cooperate with their direct neighbors and diffuse the information across the entire network through a sequence of Kalman iterations and data-aggregation. In this article, we propose the diffusion Kalman filtering with quantized global (DKFQFI)algorithm because of the limited sources in the wireless environment, where nodes exchange their quantized states with neighbors to reduce the consumption of resources. To prove the convergence of the DKFQFI algorithm, we derive the theoretical expressions of the mean and mean-square performance. From the expressions, we show that the mean performance and mean-square performance of the proposed algorithm are unbiased and stable. Therefore, the feasibility of the algorithm is verified. Moreover, the proposed algorithm achieves an outperform by simulation.

The newly rising of dynamic adaptive streaming over HTTP (DASH) enables consumers access diverse bit rate encoded video content according to the link situation, which improves the quality of experience (QoE) of consumers in mobile environment. While Content-Centric Mobile Networks (CCMNs) bring content centric design to mobile environment and has already become a promising solution for the content-based applications. The major challenges of DASH in CCMNs are how to determine proper video quality in a mobile environment and to reduce broadcasting storm. To address these problems, we design a novel adaptive video streaming solution in CCMNs (DAS-CCMN). We first analyze the load degree of content carrier, and define an interest satisfied potential (ISP) concept to reflect the ability that a content carrier can satisfy the interest request from a consumer. In DAS-CCMN, each mobile node shares its ISP information with neighbors. All nodes store this information in their interest satisfied potential table (ISPT). Based on information recorded in ISPT, a self-learning based rate determination strategy is presented to choose video content with proper bit rate. Moreover, an interest flooding control is presented to solve the broadcast storming problem for adaptive video streaming in CCMNs. Simulation results show our solution improves the performance of current DASH service in CCMNs.

Multipath TCP (MP-TCP) algorithms are supposed to be fair to single-path TCP and utilize multiple paths to support high quality end-to-end services. Existing MP-TCP algorithms in literature are confronted with the problems: 1) MP-TCP can be excessively aggressive towards single-path TCP in their coexisting environments; 2) they sometimes fail to balance congestion on multiple paths; 3)their performance is prone to degradation under the circumstances of path heterogeneity, wireless network environments and bandwidth-intensive applications. In this paper, we propose a fluid-based MP-TCP algorithm to address the above problems. Our algorithm has a unique stable equilibrium and serves the design goals of fairness and congestion-balancing. Simulation results show that our algorithm achieves improvement of TCP-friendliness and window fluctuation performance in different scenarios.

Multipath congestion control algorithms are supposed to be friendly to traditional single-path TCP (SP-TCP). Existing multipath Additive Increase Multiplicative Decrease algorithms (MP-AIMD) for multipath TCP (MP-TCP) are confronted with the problems: 1) they are unfair to SP-TCP when the multiple available paths have heterogeneity, e.g. different RTT; 2) they all use packet losses as congestion signals and induce spurious backoffs on a wireless link with high error rate; 3) congestion window fluctuation reduces their potential to support smooth high-quality service such as multimedia applications. This paper proposes a path quality based multipath AIMD (AIMD-PQ) to tackle the above problems. We utilize the round trip time (RTT) on each sub-path to formulate the path quality estimation. AIMD-PQ balances the loads among its sub-paths, moves traffic off the most congested sub-path, and triggers Multiplicative Decrease by both packet loss and path quality signals. Simulation results show that AIMD-PQ improves both the TCP-friendliness and window fluctuation performance of MP-TCP in a heterogeneous wireless network environment.

The growing availability of various wireless access technologies promotes increasing demand for mobile video applications. Stream control transmission protocol (SCTP)-based concurrent multipath transfer (CMT) improves the wireless video delivery performance with its parallel transmission and bandwidth (BW) aggregation features. However, the existing CMT solutions deployed at the transport layer only are not accurate enough due to lower layer uncertainties, such as variations of the wireless channel. In addition, CMT-based video transmission may use excessive BW in comparison with the popular Transmission Control Protocol (TCP)-based flows, which results in unfair sharing of network resources. This paper proposes a novel cross-layer fairness-driven (CL/FD) SCTP-based CMT solution (CMT-CL/FD) to improve video delivery performance, while remaining fair to the competing TCP flows. CMT-CL/FD utilizes a cross-layer approach to monitor and analyze path quality, which includes wireless channel measurements at the data-link layer and rate/BW estimations at the transport layer. Furthermore, an innovative window-based mechanism is applied for flow control to balance delivery fairness and efficiency. Finally, CMT-CL/FD intelligently distributes video data over different paths depending on their estimated quality to mitigate packet reordering and loss, under the constraint of TCP-friendly flow control. Simulation results show how CMT-CL/FD outperforms existing solutions in terms of both video delivery performance and TCP-friendliness.

Stream Control Transport Protocol (SCTP)-based multimedia streaming has gained variety of attentions and resulted in many peer-reviewed publications. However, there is no MAC-SCTP cross-layer path switching strategy appropriate for wireless networking, where wireless error tends to occur frequently due to the intrinsic wireless link characteristics. As a remedy, we in this paper propose a novel Cross-layer Cognitive SCTP (SCTP-C2) for efficient multimedia data delivery by jointly considering the characteristics of MAC layer and transport layer. A Cross-layer Path Switching Trigger (CPST) is designed in SCTP-C2to improve the efficiency of the path switching mechanism and further provide an optimal congestion window (cwnd) fast recovery scheme after path switching. A Congestion-aware Multimedia Data Distributor (CMDD) is introduced in SCTP-C2to overcome a "hot-potato" congestion problem and enable an optimal transmission behavior by identifying network congestion. The results gained by a close realistic simulation topology show that how SCTP-C2outperforms existing SCTP protocol in terms of consumers' experience of quality for multimedia streaming service.

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.

The packet loss and handover tend to occur often in burst in heterogeneous wireless network. The Sender-based transport control mechanisms make current SCTP cannot provide an expected adaptive transmission rate adjustment and recovery strategy to ensure the users' quality of experience for multimedia streaming service due to the abrupt and frequent transmission rate fluctuation. Moreover, current SCTP solutions seldom consider balancing the overhead and sharing the load between the sender and receiver. In this paper, we propose a novel receiver-driven SCTP-based multimedia delivery solution which runs some important functions at receiver including: 1) appropriate sending rate estimation and advertisement, supported by a designed receiver-based sending rate estimator; and 2) primary path selection and fast recovery, enabled by a developed receiver-assisted path switch trigger. The simulation results show that how the proposed solution outperforms existing SCTP protocol in terms of multimedia delivery performance.

Online content propagation gives rise to tremendous data explosion and requires efficient management for a large amount of network resources, after next generation network service predomination. Especially in the age of social network, the way of content propagation and consumption has significantly changed from requesting to sharing. Since massive users are tending to influenced by the trends in social community and mainstream media, content caching becomes an effective method for providing better quality of service for such social relationships. A key challenge is traditional caching strategies cannot meet the dynamic variation in geo-social environments. In this paper, we propose a dynamic social content caching scheme with social user mobility. We employ a combination of cooperative filtering recommendation and cache update algorithm to effectively predict and manage cache updates. Simulation results show that our caching method over performs than classical caching methods that rely on the historical popularity prediction.

Routing in mobile ad hoc networks (MANETs) is an extremely challenging issue due to the features of MANETs. In this paper, we present a novel bio-inspired hybrid routing protocol (B-iHRP) supporting multimedia delivery based on zone routing framework, ant colony optimization (ACO) and physarum autonomic optimization (PAO). B-iHRP divides network topology into a series of zones subjectively. Within a zone, the route table of central node is proactively maintained by perceptive ants which can sense link status metrics through cross-layer perception to assess the discovered routes. Among zones, perceptive ants are sent to reactively find routes to destinations as well as assess the discovered routes with the metrics by source nodes. Afterwards, B-iHRP uses PAO to select the optimal one from the found routes and optimize autonomically the local routes during the course of multi-zone communication sessions. Simulation results show how B-iHRP can achieve the effective performance compared to existing state-of-the-art algorithms.

Content-Centric Networking (CCN) is an entirely novel networking paradigm, in which packet forwarding relies upon lookup operations on content names directly instead of fixed-length host addresses. The unique features of CCN names, i.e., variable length, huge cardinality, and hierarchical structure, introduce new challenges that could hinder the deployment of such a new architecture at the Internet scale. In this paper, we make an in-depth study of characteristics of large-scale CCN names, and propose a simple yet efficient CCN-customized name lookup engine (named by TB2F), which capitalizes the strengths of Tree-Bitmap (TB) and Bloom-Filter (BF) mechanisms, while counteracts their main limitations. To this end, TB2F splits CCN prefix into a constant size T-segment and a variable length B-segment with a relative short length, which are treated using TB and BF, respectively. Furthermore, an optimal length of the T-segment is found to improve the lookup efficiency. Experimental comparisons with respect to the reference Name Prefix-Trie and Bloom-Hash have been also carried out. The results show that TB2F properly configured has good scalability and efficiency by (i) speeding up lookup operations and reducing the false positive rate with respect to Bloom-Hash; (ii) requiring less memory than Name Prefix-Trie; (iii) achieving a low overhead in updating operations in the large scale case.

As a promising solution to the scalability issue of the current routing infrastructure, locator/identifier separation has gained variety of attentions and resulted in thousands of peer-reviewed publications. However, there is still significant ongoing work addressing many challenges of the secure Locator/Identifier Separation Context (LISC). In this paper, we propose a novel Cognitive Adaptive Access-Control solution (CAAC) for a secure LISC with three modules, which are Tag-aware Access-Control module (TAC) that devotes to generate user tag (UTag) and service tag (STag) by cognizing their natural and dynamic attributes, Adaptive Policy Generation paradigm (APG) that serves to select proper policy instance for adaptive and intelligent access control, and Cooperative Decision Making module (CDM) that contributes to provide efficient decision-making by multi-peer parallel cooperation. We implement the designed CAAC in our identifier-based network platform to verify its advantages.

In this paper, we propose TCP-friendly CMT, a novel TCP-friendly Stream Control Transmission Protocol (SCTP)-based Concurrent Multipath Transfer (CMT) solution necessitating the following aims: (i) fairness to TCP flows, (ii) load sharing, and (iii) improve multimedia delivery performance. To satisfy the first requirement, a Weighted Moving congestion window (WM-cwnd) based Additive Increase and Multiplicative Decrease (AIMD)-enhanced congestion control mechanism is designed to make TCP-friendly CMT preserve fairness to TCP flows. A newly WM-cwnd-based data distribution algorithm is further introduced in TCP-friendly CMT to make proper load sharing and improve multimedia delivery performance. Finally, a proposal for saving energy is introduced. The simulation results show how the proposed TCP-friendly CMT solution improves the data delivery performance, as well as users' quality of experience for multimedia streaming service while still remaining fair to the competing TCP flows.

Concurrent Multipath Transfer (CMT), enabled by Stream Control Transmission Protocol (SCTP), is considered as one preferred data-transport mode due to increased available bandwidth. However, CMT performance degrades seriously in terms of data reordering due to path dissimilarity and frequent packet loss from wireless unreliability. Most relevant solutions follow the packet sequence numbers and thereby focus on strict in-order reception and packet-specific retransmission. Passively adapting to the network conditions, those approaches are not general and well enough responding to the dynamicity of wireless environment. By applying Network Coding (NC) to CMT, this paper proposes a progressive end-to-end solution (CMT-NC) to those problems in heterogeneous wireless networks. CMT-NC is capable of avoiding reordering and compensating lost packets. Further, an innovative group-based transmission management mechanism enhances the robustness and reliability of data transfer. Simulation results show how by using CMT-NC significant improvements in comparison to another state-of-the-art solution are obtained.

In this paper, we present a novel bio-inspired trusted routing framework (B-iTRF) which composed of trust mechanism and routing strategy. For trust mechanism, B-iTRF monitors neighbors' behavior in real time and then assesses neighbors' trust value based on the priori knowledge. For routing strategy, each node finds routes to the Sink based on ant colony optimization. In the process of path finding, B-iTRF senses and calculates the metrics of the found routes to support the route selection. Moreover, B-iTRF also assesses the availability of route based on Physarum autonomic optimization to maintain the route table. Simulation results show that B-iTRF can achieve the effective performance compared to existing state-of-the-art algorithms.

High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching.

Extension for the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) can achieve bandwidth aggregation by making use of parallel transmisson over selected paths. However, if CMT-based path selection depended solely upon the information provided by transport layer, it cannot really make the desired bandwidth aggregation. Motivated by the urgent needs of cross-layer bandwidth aggregation and the advances of Ant Colony Optimization (ACO) in network selection, this paper proposes a novel ACO based cross-layer bandwidth aggregation scheme for efficient Concurrent Multipath data Transfer (CMT-ACO) in wireless transmission. CMT-ACO provides an efficient data delivery with two modules, which are ACO-based Efficiency Aware model (ACO-EA) that devotes to sense paths' transmission efficiency(supported by a cross-layer factor) and reduce ``ping-pongquot; path switching (enabled by a stabilization factor), and ACO-based Bandwidth Aggregation scheme (ACO-BA) that contributes to provide a cross-layer optimal bandwidth aggregation scheme. The results gained by a close realistic simulation topology show that how CMT-ACO outperforms existing CMT protocol in terms of performance and quality of service in multi-homed SCTP-based wireless networks.

There is a trade-off between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism-slime mold physarum polycephalum, this paper presents a novel physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node selects its proper next hop by using a proposed physarum-inspired selecting next hop model (PSN), which considers comprehensively the distance, energy residue and location of the next hop. We introduce the PSN's routing selecting strategy and detail PiRP's algorithms. Simulation results show how P-iRP can achieve the effective trade-off between routing efficiency and energy equilibrium compared to existing classical algorithms.

With feature of flows across multiple interfaces based on the multi-homing feature of Stream Control Transport Protocol (SCTP), Concurrent Multipath Transfer (CMT) has been regarded as a promising protocol for content-rich multimedia data delivery under stringent bandwidth, delay, and loss wireless environment. However, current CMT researches mostly pay attention to improve CMT protocol itself depended solely upon the information provided by transport layer. In this paper, we propose a novel MAC-SCTP based Cross-layer Cognitive CMT (CMT-CC) for efficient multimedia distribution in varying wireless transmission. A Cross-layer Quality Sense Model (CQSM) is designed in the CMT-CC to cognize the paths' quality and select candidate paths for multimedia content delivery. By condition-aware cognitive ability to distinguish the causes of transmission condition change, a further proposed Intelligent Multimedia Content Distributor (IMCD) makes adaptive multimedia delivery behaviors in compliance with real-time wireless condition. Results obtained by a close realistic simulation topology show how the CMT-CC outperforms existing CMT approach in terms of users' of quality of experience for multimedia streaming services.

Information-Centric Networking (ICN) is a clean-slate networking architecture that puts information is focus instead of addressed hosts. Construction of content retrieval model to estimate the delivery performance is challenging in this ICN-based Mobile Ad hoc Networks (MANETs). In this paper, we propose a novel content retrieval model (PRCRM) for Information-Centric MANETs (ICMs) in 2-dimensional case. By investigating the distribution of content popularity, receiver-driven mechanism, content caching and replacement mechanism and generalized mobility model in 2-dimensional space, PRCRM constructs a novel content retrieval model based on the content hit/miss probability to estimate the content retrieval-related performance. We evaluate PRCRM by comparing its performance with another state of the art solution in terms of RTT and throughput. Simulation results demonstrate PRCRM's rationality and validity and it is shown that PRCRM is available to analyze content retrieval in ICMs.

Mobile devices equipped with multiple network interfaces can increase their throughput by making use of parallel transmissions over multiple paths and bandwidth aggregation, enabled by the stream control transport protocol (SCTP). However, the different bandwidth and delay of the multiple paths will determine data to be received out of order and in the absence of related mechanisms to correct this, serious application-level performance degradations will occur. This paper proposes a novel quality-aware adaptive concurrent multipath transfer solution (CMT-QA) that utilizes SCTP for FTP-like data transmission and real-time video delivery in wireless heterogeneous networks. CMT-QA monitors and analyses regularly each path's data handling capability and makes data delivery adaptation decisions to select the qualified paths for concurrent data transfer. CMT-QA includes a series of mechanisms to distribute data chunks over multiple paths intelligently and control the data traffic rate of each path independently. CMT-QA's goal is to mitigate the out-of-order data reception by reducing the reordering delay and unnecessary fast retransmissions. CMT-QA can effectively differentiate between different types of packet loss to avoid unreasonable congestion window adjustments for retransmissions. Simulations show how CMT-QA outperforms existing solutions in terms of performance and quality of service.

The aggregate capacity of wireless mesh networks can be increased by the use of multiple channels. In this paper, we present the joint channel assignment implementation for multi- interface and multi-channel wireless network. To reap the full performance potential of this architecture, we propose and evaluate a combination of centralized and dynamic peer oriented distribution channel assignment, and enhanced AODV routing algorithms for real time multi-channel wireless mesh networks. Simulation results show that with joint channel assignment, equipping every wireless mesh network node with different interfaces operating on different channels can improve the total network performance and make the most of traffics by finding more routes with enhanced AODV.

Recent research on concurrent multipath transfer (CMT) and CMT with a potentially-failed destination state (CMT-PF) uses the transport layer multi-homing protocol stream control transmission protocol (SCTP) to increase application throughput by distributing transmitted data across multiple end-to-end paths. This paper investigates and evaluates the performance of CMT with partial reliability (CMT-PR) and CMT-PF with partial reliability (CMT-PF-PR), novel extensions of SCTP for real-time video distribution. The Evalvid-CMT platform was implemented in the University of Delaware's SCTP/CMT ns-2 module to perform emulation experiments in order to compare CMT and CMT-PR, CMT-PF and CMT-PF- PR, respectively. The results presented in the paper show how the CMT-PR and CMT-PF-PR outperform CMT and CMT-PF respectively. Consequently the former are suggested as strategies for real-time video concurrent multipath transmissions.

Most research on P2P multimedia streaming assumes that users access video content sequentially and passively. Unlike P2P live streaming in which the peers start playback from the current point of streaming when they join the streaming session, in P2P video-on-demand streaming VCR-like operations such as forward, backward, and random-seek have to be supported. Providing this level of interactive streaming service in a P2P environment is a significant challenge. This paper proposes a balanced binary tree-based strategy for unstructured video-on- demand distribution in P2P networks (BBTU). BBTU assumes videos can be divided into several segments which can be fetched from different peers. BBTU involves two steps: 1) balance binary tree construction based on a prefetching algorithm in order to support interactivity; 2) unstructured video dissemination over network based on gossip protocol, which is the overlay for video distribution. Analysis and simulation show how BBTU is an efficient interactive streaming solution in P2P environment.

It requires an enormous amount of media server and network resources for on-demand delivery of video objects to a large number of clients with high start-up latency and packet loss ratio. An effective solution is to deploy a streaming proxy server close to client to cache media streaming data. In this paper, we propose an integral and scalable data sharing scheme, Optimal Prefix Caching and Data Sharing (OPC-DS), which combines prefix caching and interval caching in the proxy cache. In OPC-DS, the sizes of the appropriate prefix cache and interval cache are calculated according to the current request distribution. It can reduce the average start-up delay experienced by users as well as assure that the major of requests can share the data in the proxy buffer. So, utility of the proxy resources can be improved, and the resources consumption can be reduced significantly for the media server and back-bone network bandwidth by using it. Experimentally comparing with several existing methods, OPC-DS achieves significant performance improvement.

It requires an enormous amount of server and network bandwidth resources for providing real-time video streams to a large number of clients. And when using variable-bit-rate (VBR) encoded streams, frequent traffic bursts of bit rate have high variability for resource requirements. In this paper, we propose a smoothing of prefix caching and data sharing (S-PCDS) scheme based on the proxy caching, which can assist in smoothing operation for VBR encoded streams and reduce the start-up delay of users, through caching video prefix in the proxy. It can share the data in the buffer of proxy through interval caching which size is determined dynamically according to the current request distribution. With S-PCDS, the peak requirements of the resources can be reduced for VBR stream, the major of requests can share the data in the buffer, utility of the resource can be improved for the proxy, and the resource consumption can be minimized for the media server and back-bone network bandwidth. These are proved by comparing with several existing methods experimentally.

The existing DONet-based approach uses successfully a random gossip algorithm for scalable live video streaming. This pure mesh overlay network-based solution may lead to unacceptable latency or even failure of VCR operations in video-on-demand (VoD) services where nodes usually have different playing offsets, across a wide range. This paper proposes DONet-VoD which enhances DONet in order to address issues related to VoD delivery and VCR operations. In DONet-VoD, DONet principle is employed for the video distribution over the overlay network and a novel algorithm which uses a multi-way tree structure and extra prefetching buffers at the nodes is proposed to support efficient VoD operations. Video segments are prefetched and stored in a distributed manner in the nodespsila prefetching buffer along the tree. The cooperation between DONet-based video delivery and the tree-located multimedia components enable multimedia streaming interactive commands to be performed efficiently. This paper presents and discusses the prefetching scheme, details the cooperation procedure, and then analyses the performance of the proposed DONet-VoD.

In this paper we firstly improve a virtual network buffer model (VB), based on which we propose a joint sender/receiver rate control algorithm for scalable video streaming. A transmission rate is determined by a rate control algorithm at the sender which employs the program clock reference (PCR) embedded in the video streams to work in a refined way. An over-boundary playback rate adjustment mechanism based on proportional-integral (PI) controller is performed at the receiver to maximize the visual quality of the displayed video according to the receiver buffer occupancy. Simulation results show that our proposed algorithm can reduce the overflow and underflow of the sender and receiver buffer, and achieve better video quality and quality smoothness than traditional rate control algorithms.

In order to obtain better video quality, media files are required to use variable-bit-rate (VBR) encoding. However, it produces traffic burst and unbalanced resource utilizations to translate VBR-encoded video. In this paper, we propose a novel bandwidth smoothing algorithm, buffer sharing and bandwidth smoothing of VBR streams (BSBS-VBR), which combines with prefetching and interval caching, allows video server to transmit a VBR-encoded stream at a fixed rate and makes users to share a disk stream. BSBS-VBR can also allocate and adjust buffer size dynamically according to the current request distribution and available resources. It can reduce the peak requirements of disk bandwidth and network bandwidth, improve utility of the resources, and serve more users by using this algorithm. These conclusions are proved by comparing with several existing methods experimentally.

Most of the existing buffer allocation and sharing schemes can not make full use of the system resources while being implemented independently. Here, based on analysis of the existing schemes, we propose a new algorithm, Balanced Buffer Sharing of Limited Resource (BBSLR), which allocates resources for each request according to the available cache and disk bandwidth, and adjusts buffer size according to the current request distribution and available resources dynamically. It does a good job of managing resources to maximize the number of simultaneous clients and enhance start-up delay. With BBSLR, the resources consumption will be balanced by rational allocation of the available resources, the average start-up delay will be reduced by caching the data at start and more clients will be served consequently. These conclusions are proved by comparing with several existing methods experimentally.

In this paper we present a video communication scheme that integrates rate control and buffer management at the source. A transmission rate is obtained via a rate control algorithm, which employs the Program Clock References (PCR) embedded in the video streams to regulate the transmission rate in a refined way and thus reduce the client buffer requirement. The server side also maintains multiple buffers to trade off random loss for controlled loss of visually less important data. We test our system with Standard Definition Television (SDTV) and High Definition Television (HDTV) traces, and find that the proposed scheme serves best for the transmission by slowing down the transmission rate without higher buffer requirement in both cases.

In this paper, we present a practical end-to-end video transmission system with refined rate allocation at the server and adaptive playback adjustment at the client that enables High Definition (HD) video streaming via bandwidth-constraint IP network. A transmission rate is determined by a rate control algorithm which employs the Program Clock References (PCR) embedded in the video streams to regulate the transmission rate in a refined way and thus reduce the client buffer requirement. An over-boundary playback adjustment mechanism based on Proportional-Integra (PI) controller is performed at the receiver to maximize the visual quality of the displayed video according to the receiver buffer occupancy. We test our system with Standard Definition Television (SDTV) and High Definition Television (HDTV) traces, and find that our proposed algorithm can reduce overflow and underflow of sender and receiver buffer, and achieve better video quality and quality smoothness than traditional rate control algorithms.