Imitation learning provides an efficient paradigm for acquiring robotic manipulation skills, yet policies trained in a single environment often generalize poorly to unseen scenes. To address this challenge, we propose CoVA-IL, a zero-shot imitation learning framework that performs contrastive viewpoint alignment on object-centric representations, enabling direct policy deployment in novel environments without retraining. CoVA-IL uses the target object’s point cloud as the visual input and learns viewpoint-invariant latent representations through contrastive learning, thereby improving robustness to background changes, viewpoint variations, and cross-environment shifts. In addition, we incorporate multi-level point-cloud augmentation into 3D visuomotor imitation learning to improve data efficiency and reduce the number of demonstrations required for training. Real-world experiments show that CoVA-IL maintains average task success rates of 82.5% and 80% under substantial background and viewpoint changes, respectively, and multi-scene evaluations further validate its effectiveness for cross-environment deployment. Moreover, CoVA-IL can learn basic manipulation skills from a small number of real demonstrations, thereby reducing demonstration collection costs.

The surge of artificial intelligence (AI) has intensified compute-intensive tasks, sharply increasing the need for energy-efficient management in geo-distributed data centers. Existing approaches struggle to coordinate task scheduling and cooling control due to mismatched time constants, stochastic Information Technology (IT) workloads, variable renewable energy, and fluctuating electricity prices. To address these challenges, we propose D3T, a dual-timescale deep reinforcement learning (DRL) framework that jointly optimizes task scheduling and thermal management for energy-efficient geo-distributed data centers. At the fast timescale, D3T employs Deep Q-Network (DQN) to schedule tasks, reducing operational expenditure (OPEX) and task sojourn time. At the slow timescale, a QMIX-based multi-agent DRL method regulates cooling across distributed data centers by dynamically adjusting airflow rates, thereby preventing hotspots and reducing energy waste. Extensive experiments were conducted using TRNSYS with real-world traces, and the results demonstrate that, compared to baseline algorithms, D3T reduces OPEX by 13% in IT subsystems and 29% in cooling subsystems, improves power usage effectiveness (PUE) by 7%, and maintains more stable thermal safety across geo-distributed data centers.

Low Earth orbit (LEO) satellite communication systems increasingly rely on large-scale constellations, which intensifies spectrum competition, interference management, and scheduling overhead. Existing multi-satellite beam-hopping schemes are mostly based on centralized control, where global demand collection, ground-based scheduling, and command delivery may introduce excessive control delay and limited scalability. To address these issues, this paper proposes an autonomous control framework for LEO multi-satellite systems based on orbital edge computing. In this framework, beam scheduling is offloaded from the ground control center to onboard satellites, enabling local perception and autonomous decision-making while explicitly reducing the propagation-related control delay caused by demand collection and command delivery. Based on this framework, a pheromone-enhanced graph neural network deep reinforcement learning algorithm is developed to optimize cooperative beam hopping under local observations. The proposed algorithm combines pheromone-based service memory, graph neural network-based state representation, and reward-sharing Double DQN to achieve implicit multi-satellite cooperation without explicit inter-satellite information exchange. Simulation results show that the proposed method outperforms representative baselines in terms of throughput, queueing delay, demand satisfaction, and interference suppression. Under high traffic load, PE-GDRL improves system throughput by at least 12%, reduces average queueing delay by about 9%–10%, and achieves nearly 100% demand satisfaction under different coverage conditions. Theoretical analysis further shows that the proposed framework provides better control timeliness and scalability than centralized control architectures.

Multi-beam low earth orbit (LEO) satellites, with their wide coverage, high communication rates, low latency, and flexibility, are essential components in the 5G and 6G eras. However, challenges such as uneven ground user distribution, multi-dimensional resource coupling, inter-beam interference, and dynamic network topology complicate efficient resource management. This paper presents a joint beam hopping and resource allocation (BHRA) scheme within a Digital Twin (DT)empowered multi-beam LEO satellites network. The complex resource management problem is divided into two sub-problems: traffic-satellite allocation and joint multi-satellite BHRA. First, cell traffic is allocated among satellites to balance load using deep reinforcement learning (DRL). Then, Hierarchical Proximal Policy Optimization (HPPO) optimizes multi-satellite beam hopping and resource allocation to meet demand. Extensive simulation results demonstrate that the proposed method reduces satellite load imbalance by approximately 83%, achieves the highest throughput compared to other algorithms, and generalizes well as traffic demand increases.

ABSTRACT Homography estimation is a fundamental topic in computer vision, especially in scenarios that require perspective changes for intelligent analysis of sports fields, where it plays a crucial role. Existing methods predict the homography matrix either indirectly by evaluating the 4‐key‐point coordinate deviation in paired images with the same visual content or directly by fine‐tuning the 8 degrees of freedom numerical values that define the matrix. However, these approaches often fail to effectively incorporate coordinate positional information and overlook optimal application scenarios, leading to significant accuracy bottlenecks, particularly for paired images with differing visual content. To address these issues, we propose an approach that integrates both methods in a staged manner, leveraging their respective advantages. In the first stage, positional information is embedded to enhance convolutional computations, replacing serial concatenation in traditional feature fusion with parallel concatenation, while using 4‐key‐point coordinate deviation to predict the macroscopic homography matrix. In the second stage, positional information is further integrated into the input images to refine the direct 8 degrees of freedom numerical predictions, improving matrix fine‐tuning accuracy. Comparative experiments with state‐of‐the‐art methods demonstrate that our approach achieves superior performance, yielding a root mean square error as low as 1.25 and an average corner errror as low as 14.1 in homography transformation of competitive sports image pairs.

Due to the uneven distribution of ground communication demands and the multi-coverage characteristics of Low Earth Orbit (LEO) satellites, a key challenge arises in aligning the limited onboard satellite resources with the heterogeneous ground demands through multi-satellite collaboration. Traditional approaches, which rely on centralized beam coverage planning by ground control centers, often result in scheduling delays and increased communication overhead, thereby constraining system responsiveness and flexibility. This paper proposes a neighboring satellite beam coordination framework based on satellite autonomous control, enabling local satellite decisionmaking for beam coverage and real-time responses to resource allocation demands. Specifically, the framework is divided into two phases: the prediction-approximation phase and the strategy decision phase. In the prediction-approximation phase, satellites predict current communication demands based on historical traffic data from ground cells and approximate the channel state information using easily observable elevation angle data. In the strategy decision phase, a neighboring satellite collaborative beam-hopping algorithm, based on global information sharing and independent policy reinforcement learning, is proposed. This approach enables beam coordination between neighboring satellites without the need for inter-satellite communication. In a twosatellite neighboring coordination system, the proposed method achieves a throughput improvement of 5.78%–28.54% and a delay reduction of 2.63%–26.56% compared to baseline schemes. Relative to ground control center-based approaches, the proposed method significantly reduces control latency and enables faster response. Further scalability and limitations analysis confirms that the proposed framework provides efficient, stable, and robust resource scheduling, with excellent scalability characteristics.

Low-Earth orbit (LEO) satellites utilizing beam hopping (BH) technology offer extensive coverage, low latency, high bandwidth, and significant flexibility. However, the uneven geographical distribution and temporal variability of ground traffic demands, combined with the high mobility of LEO satellites, present significant challenges for efficient beam resource utilization. Traditional BH methods based on GEO satellites fail to address issues such as satellite interference, overlapping coverage, and mobility. This paper explores a Digital Twin (DT)-based collaborative resource allocation network for multiple LEO satellites with overlapping coverage areas. A two-tier optimization problem, focusing on load balancing and cell service fairness, is proposed to maximize throughput and minimize inter-cell service delay. The DT layer optimizes the allocation of overlapping coverage cells by designing BH patterns for each satellite, while the LEO layer optimizes power allocation for each selected service cell. At the DT layer, an Actor-Critic network is deployed on each agent, with a global critic network in the cloud center. The A3C algorithm is employed to optimize the DT layer. Concurrently, the LEO layer optimization is performed using a Multi-Agent Reinforcement Learning algorithm, where each beam functions as an independent agent. The simulation results show that this method reduces satellite load disparity by about 72.5% and decreases the average delay to 12ms. Additionally, our approach outperforms other benchmarks in terms of throughput, ensuring a better alignment between offered and requested data.

Vehicular named data networking (V-NDN) is promising to match dynamic topology of vehicular ad hoc networks. However, it has some potential issues such as broadcast storm of Interest packets due to flooding and blind forwarding strategy at intermediate nodes. To address these issues, we propose a network coding enhanced forwarding strategy with predictive content discovery (NC-PCD) mechanism which functions at key nodes (e.g., road intersections). First, content providers provide innovative encoded data packets. Second, the NDN packet formats are extended to carry the information about the innovative degree, distance, congestion level and path quality, etc. Finally, the NDN router at the key node collects information to select the optimal forwarding direction for subsequent requests. This problem is formulated as a joint optimization problem and solved by deep reinforcement learning. Simulation results show that the proposed NC-PCD is significantly superior to the existing forwarding strategies in terms of Interest satisfied ratio, response delay and number of transmitted packets.

The Low Earth Orbit (LEO) satellite constellation has been recognized as an important component of the future 6G network. However, due to the high-speed movement of LEO satellites and the potential for link failures, achieving optimal satellite communication performance with a static inter-satellite links (ISLs) scheme is challenging. To solve this problem, this paper proposes an ISL re-planning algorithm with considering link failures based on multi-agent deep reinforcement learning (named ReISL). In ReISL, a multi-objective optimization problem is formulated to maximize the system capacity while minimizing the link switching costs. Then, multi-agent deep reinforcement learning is employed to derive the optimal ISL re-planning schemes, where each satellite utilizes Double Deep Q-Network (DDQN). Finally, extensive experiments are carried out and the results demonstrate that our proposed algorithm ReISL can outperform the baseline algorithms.

Earth observation satellites in low earth orbit (LEO) collect a large amount of image data daily, while space-to-ground links have become the major bottleneck for data transmission due to the limited bandwidth. Existing approaches focus on exploring more efficient routing strategies to achieve better data transmission but still struggle to keep pace with the surging volume of observed data. However, the advancement of onboard computing power has opened the possibility of processing data on satellites to reduce the transmitted data volume. This paper proposes a distributed deep reinforcement learning (DRL) algorithm to improve transmission efficiency by jointly optimizing computing and routing. Aiming to minimize task latency while considering the limitations of satellite storage resources, the problem is modeled as a partially observable Markov process (POMDP). An algorithm based on dueling double deep Q-Network (Dueling-DDQN) is proposed to achieve dynamic decision-making utilizing local and neighboring resource states. Furthermore, a method for dynamic optimization of backhaul destinations is proposed, using the pre-trained Q-network to estimate action values across multiple candidate destination satellites, thus enabling further optimization of data transmission without additional training. Simulation results indicate that the proposed algorithm achieves the lowest latency across various task loads compared to baseline methods.

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent difference reward based actor-critic with V2I bandwidth allocation (MDRAC-VBA) solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of MDRAC-VBA.

High-quality video can provide viewers with a better visual and immersive experience, but it typically requires a significantly higher network bandwidth to accommodate the larger amount of video data. The existing and commonly-used adaptive bitrate (ABR) approaches cannot provide high-quality video services for viewers when the network is poor. To address this issue, we propose an edge super-resolution assisted adaptive video streaming, named SuperABR, to improve viewers' Quality of Experience (QoE) and mitigate the influence of poor networks. First, to prevent the mismatch between the available computing resources and the VSR workload, the dynamics and trends of the available computing capability are extracted from a series of historical VSR reconstructing delays. Second, to optimize the video quality, rebuffering time, and video quality jitter for viewers, we formulate the optimization model by fully considering the states of all caching queues in SuperAbr and imposing probability constraints on the playback queue. Third, a queue-learning-based adaptive video streaming algorithm is proposed in SuperABR to jointly determine the source transmission resolution and the edge VSR-reconstructed resolution, which is essentially a Deep Reinforcement Learning (DRL) method with queue constraints. Finally, extensive experiments illustrate that SuperABR can improve QoE by 20%-76% compared to four baseline algorithms.

The Low Earth Orbit (LEO) satellite constellation has been recognized as an important component of the future 6G network. Due to the high speed movement and limited on-board energy of LEO satellites, as well as the uneven distribution of service requests on the ground, it is difficult to achieve optimal satellite communication performance using a static inter-satellite links (ISLs) scheme and a fixed transmission power per link. To solve this problem, this paper proposes a joint optimization algorithm based on parameterized deep reinforcement learning (named DeepISL) for dynamic planning of ISLs between different planes and transmit power allocation per link. First, a partially observable Markov decision process (POMDP) is established by modeling the communication, energy, and overall energy efficiency as well as the antenna steering costs. Second, to solve the hybrid action space problem with discrete action variables for ISL planning and continuous action variables for power allocation, deep multi-agent reinforcement learning with parameterized action space is used to obtain the optimal joint strategy. Finally, extensive experiments illustrate that our proposed algorithm can improve the energy efficiency of the constellation by 4.2% ~ 10.5% compared to the comparison algorithms, and can also achieve better performance in terms of throughput and ISLs switching ratio.

High quality video can provide viewers with better visual and immersive experience in video streaming systems. However, traditional such systems with adaptive bitrate algorithms can only provide low-resolution videos when the network conditions deteriorate. To break the strong dependency of video transmission on network, we design and demonstrate iSAW, a super-resolution-assisted intelligent adaptive WebRTC video streaming system, by using two mobile devices and a policy server. iSAW performs a lightweight and scalable super-resolution (SR) model on the mobile devices to enhance the quality of received videos, and leverages an learning-based adaptive algorithm on the policy server to jointly adjust the transmitted video resolution and SR reconstructed video resolution according to dynamic network conditions and client computing power. The enhanced real-time video is smooth and the performance is improved obviously.

Unmanned aerial vehicle (UAV)-assisted communications are playing an increasingly critical role in emergency aids and disaster recovery, thanks to their excellent rapid deployment capabilities. However, limited on-board caches and power supplies pose a serious challenge to the cooperative scheduling of multiple UAVs, with additional complexity introduced by different natures of caching decision and power adjustments. To address the issues, we formulate joint optimization of collaborative caching and power allocation as a hybrid discrete-continuous optimization problem and introduce a parameterized deep Q-networks (PA-DQN) based collaborative caching and power allocation (PA-DQN-CCPA) joint optimization algorithm, taking into consideration of emergency service models. Extensive simulations demonstrate it significantly improves cache hit ratio by 18.75%, 10.14% and reduces average delay by 21.36%, 11.96% compared to the algorithm based on deep deterministic policy gradient (DDPG) and deep Q-networks (DQN), respectively.

The variation of wireless signal in dynamic indoor parking environments may seriously compromise the performance of fingerprint-based localization methods. In this regard, this paper investigates the problem of robust WiFi fingerprint-based vehicle tracking in dynamic indoor parking environments, aiming at designing an online learning framework to continuously train the localization model and counteract the effect of signal variation. Specifically, a Hidden Markov Model (HMM) based Online Evaluation (HOE) method is firstly proposed to assess the accuracy of localization results by measuring the inconsistency of locations inferred by WiFi fingerprinting and Dead Reckoning (DR). Further, an Online Transfer Learning (OTL) algorithm is designed to improve the robustness of the fingerprinting localization, which consists of a weight allocation scheme to combine two classification models (i.e., the batch model and the online model) and an instance-based transferring scheme to resample the offline fingerprints and retrain the batch model. Finally, we implement the system prototype and give comprehensive performance evaluation, which demonstrates that the proposed solutions can outperform the state-of-the-art localization algorithms around 28%$\sim$58% on vehicle tracking accuracy in dynamic indoor parking environments.

As the Low Earth Orbit (LEO) satellite has extremely high moving speed and limited networking resources, designing dynamic routing has become a promising approach to improve satellite communication performance. Due to the hundreds of satellites within a constellation and the complex attributes of each satellite, traditional routing strategies based on centralized paradigm derivation face increasingly complex challenges. To address these issues, this paper jointly optimizes queuing delay and propagation delay by proposing a fully distributed routing algorithm based on deep reinforcement learning. Each satellite builds a partially observable Markov decision process (POMDP) model based on the spatial location and queue length of surrounding nodes and adaptively selects the next hop by calculating the estimated residual propagation delay between the neighboring satellites and the destination satellite. Simulation analysis shows that our proposed method has tremendous advantages and effectiveness.

Video services such as short video sharing have exploded due to the rapid development of Internet social media platforms. Caching video segments on satellites effectively shortens service delay and speeds up video sharing, especially for users without terrestrial Internet access. However, where to place what video and how to replace it in time is by no means an easy task, requiring careful consideration of many factors, e.g., satellite coverage, video popularity, and limited caching resource. In this paper, we propose a coverage-aware cooperative video caching algorithm (CACVC) that considers the prevalence of video in the coverage area and the collaboration between adjacent satellites. In CACVC, we model the cache placement problem of video as a Partially Observable Markov Decision Process (POMDP) to optimize the service delay of video provided by access satellites, neighboring satellites, or ground stations. We derive the optimal cache strategy by utilizing Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with a centralized training and distributed execution paradigm. Simulation results show that the cache hit ratio can be improved by 4%~18%, and the average service delay can be reduced by 1%~14%.

Low Earth Orbit (LEO) satellite constellations are promising to provide global coverage and low latency communication by deploying a large number of small satellites and widely establishing Inter-Satellite Links (ISLs). However, due to the high motion of the LEO satellites, fixed inter-plane ISLs cannot provide long-time continuous connectivities and guarantee high-throughput communication performance. The existing dynamic planning approaches almost only consider part of the constellation information and cannot derive the optimal inter-plane ISLs. This paper proposes a dynamic Inter-plane Inter-satellite Links Planning method based on Multi-Agent deep reinforcement learning (MA-IILP) to optimize the total throughput and inter-plane ISL switching rate. We formulate a Partially Observable Markov Decision Process (POMDP) model with taking into account the Euclidean distance, communication rate and link switching cost. We derive the optimal strategy by utilizing Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm with a centralized training and distributed execution paradigm. Finally, extensive experiments are carried out and the results illustrate that our proposed approach can increase the total throughput of the target constellation by 2.8%∼7.2%, and decrease the inter-plane ISL switching rate by 30.7%∼68.4% compared to the state-of-the-art baseline algorithms.

ognition in simple scenes, the difference between classes of basketball actions is slight, and the backgrounds in the video are very similar. Therefore, it is not easy to recognize the basketball actions directly based on short-term temporal information or the scene information in the video. A Global Context-Aware Network (GCA-Net) for basketball action recognition is proposed to address this problem in this paper. It contains a Multi-Time Scale Aggregation (MTSA) module and a Spatial-Channel Interaction (SCI) module to process multiple types of information on feature layers. The MTSA module uses a temporal pyramid to get contextual links in the temporal dimension through one-dimensional convolution with different dilation rates. The SCI module enhances the feature representation to obtain more prosperous category attributes and spatial information by interacting with information across dimensions. We conducted experiments on the basketball action recognition dataset SpaceJam, and the results show that GCA-Net can effectively classify basketball actions. The average recognition accuracy of ten types of basketball actions in the dataset is 91.54%, which is an improvement compared with the current mainstream methods.

Content caching in vehicular networks is a promising technology to dramatically reduce the request-response time and transmission delay. The existing caching policies often suffer from high computation and communication overhead and fail to well capture the dynamics of the vehicular networks and content popularity. In this paper, we propose a novel Cooperative Caching algorithm for vehicular networks with multi-level federated Reinforcement Learning (named CoCaRL) to dynamically determine which contents should be replaced and where the content requests should be served. In CoCaRL, Deep Reinforcement Learning (DRL) is employed to optimize the cooperative caching policy between RoadSide Units (RSUs) of vehicular networks, while a federated learning framework applies to reduce the computation and communication overhead in a decentralized way. To speed-up the convergence rate, we also develop a two-level aggregation mechanism for federated learning, where the low-level aggregation is performed at the RSUs and the high-level aggregation is executed at a Global Aggregator (GA). Through extensive simulation experiments, we demonstrate that our algorithm can: 1) achieve a higher hit rate than four baseline algorithms, 2) converge faster than original federated reinforcement learning without multi-level aggregation, and 3) perform good adaptability to different cache capacities and content quantities.

The underwater acoustic sensor network (UASN) is a specific deployment of Internet-of-Things (IoT) technology in the underwater environment, since energy constraints limit the lifetime of UASNs, effectively balancing the energy consumption of acoustic sensor nodes in UASNs is important to maximize the amount of information collected and to prolong the network lifetime. Node clustering is widely regarded as one of the most important energy-efficient schemes for UASNs. However, most existing clustering schemes focus on the cooperation-based election of cluster headers (CHs) in a centralized manner. Due to the limited energy capacity, acoustic sensor nodes are designed to save their own energy, hindering the realization of such cooperation. To address this issue in this article, game theory is applied to UASNs to balance network energy consumption and model acoustic sensor nodes as rational and selfish players. Specifically, a game-theory-based clustering (GTC) scheme for UASNs is developed. In the CH election phase, each node makes a decision in pursuit of a greater payoff based on the Nash equilibrium. An incentive mechanism is invented to induce nodes to make more beneficial collective decisions and plays a role in the CH rotation to effectively balance the energy consumption. Meanwhile, the network area is divided into nonuniform sectors to ensure the energy consumption of the CH is more evenly distributed. Simulation results show that the proposed GTC scheme can effectively balance network energy consumption and extend the network lifetime.

Named data networking (NDN) is promising for Vehicular Ad hoc Networks (VANETs) owing to its data-centric communication paradigm. However, the existing forwarding strategies for vehicular named data networks (VNDN) cannot handle well the issues of high mobility and broadcast storm. In this paper, we propose a Mobility-Predict-based Forwarding Strategy (MPFS) to tackle these issues in VNDN. First, in order to solve the problem of outdated mobility information in the neighbor table, a lightweight but highly effective approach is proposed to predict the current position in MPFS. Then, the predicted mobility information of the vehicles in the neighbor table is applied to select the next-hop forwarder(s) in both directions (road direction and reverse road direction) of the consumer. Furthermore, the vehicle that is farthest from the current forwarder with a stable link is considered as the next-hop forwarder. Finally, extensive simulations are carried out to demonstrate that MPFS has a less number of Interest packets forwarded, while maintaining a higher ratio of satisfied Interest packets compared with the baseline forwarding strategies.

Scalable High Efficiency Video Coding (SHVC) is the extension of High Efficiency Video Coding (HEVC). In intra prediction for quality SHVC, a Coding Unit (CU) is recursively divided into a quadtree-based structure from the largest 64×64 CU to the smallest 8×8 CU, in which 35 intra prediction modes and Inter-Layer Reference (ILR) mode are checked to determine the best possible mode. This leads to very high coding efficiency but also results in an extremely high coding complexity. To improve coding speed while maintaining coding efficiency, in this paper, we propose a new efficient algorithm for fast intra prediction for enhancement layer in SHVC. First, temporal and spatial correlations, as well as their correlation degrees, are combined in a Naive Bayes classifier to predict depth probabilities and skip depths with low likelihood. Second, for a given depth candidate, we combine ILR mode probability with Partial Zero Blocks (PZBs) based on the Sum of Squared Differences (SSD) to determine whether the ILR mode is the best one. In that case, we can skip intra prediction, which requires very high complexity. Third, initial Intra Modes (IMs) are obtained through Sobel operator, and are combined with the relationship between IMs and their corresponding Hadamard Cost (HC) values to predict candidate IMs in Rough Mode Decision (RMD). Then, an analytical criterion of early termination is developed based on the HC values of two neighboring IMs in the Rate-Distortion Optimization (RDO) process. Finally, we combine depth probabilities and the distribution of residual coefficients at the current depth to early terminate depth selection. The proposed scheme can significantly decrease the complexity of depth determination while reducing the complexity of mode decision for a depth candidate. Our experimental results demonstrate that the proposed scheme can achieve a speed up gain of more than 80% in average, while maintaining coding efficiency.

Uploading Virtual Reality (VR) video over cellular networks is expected to boom in near future, as general consumers could generate high-quality VR videos with portable 360-degree cameras and are willing to share with others. Con-sequently, concerns of uplink bandwidth and delay arose for current popular technology of tile-based VR video streaming, which requires high quality video to transcode into multiple representations for further adaptive streaming. Motivated by this, we proposed a novel scheme for uplink delivery of tile-based VR video over cellular network, in which encoding bit rate of each tile is determined by uplink resource allocation (RA), and quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA. Moreover, the RA problem is formulated as a frequency and time dependent non-deterministic polynomial(NP)-hard problem, which can be effectively solved by our proposed approximate convex algorithm. Simulation results show that the proposed algorithm can achieve higher utility, that is higher total quality of experience (QoE) for viewers.

The emergence of edge computing has witnessed a fast-growing volume of data on edge devices belonging to different stakeholders which, however, cannot be shared among them due to the lack of the trust. By exploiting blockchain's non-repudiation and non-tampering properties that enable trust, we develop a blockchain-based big data sharing framework to support various applications across resource-limited edges. In particular, we devise a number of novel resource-efficient techniques for the framework: (1) the PoC (Proof-of-Collaboration) based consensus mechanism with low computation complexity which is especially beneficial to the edge devices with low computation capacity, (2) the blockchain transaction filtering and offloading scheme that can significantly reduce the storage overhead, and (3) new types of blockchain transaction (i.e., Express Transaction) and block (i.e., Hollow Block) to enhance the communication efficiency. Extensive experiments are conducted and the results demonstrate the superior performance of our proposal.

With the emergence of edge computing in various applications domains, end users are now surrounded by a fast growing volume of data from edge devices belonging to different stakeholders. However, these edge devices cannot cooperate to share big data because of the distrust among them. In this paper, the blockchain is deployed in collaborative edges by exploiting the non-repudiation and non-tampering properties to enable trust. First, we develop a blockchain based big data sharing framework in collaborative edges for adapting to the limited computational and storage resources in edge devices. Then, a consensus mechanism called Proof-of-Collaboration (PoC) is proposed for computational resources reduction in our proposed framework, where edge devices offer their credits of PoC to compete for the block generation. Moreover, we put forward a futile transaction filter algorithm for transaction offloading, greatly reducing the storage resources occupied by the blockchain in edges. Extensive experiments are performed to demonstrate the superior performance of our proposal.

Machine-to-machine (M2M) communications and applications are expected to be a significant part of the next generation 5G networks. While there have been a large amount of research studies with respect to radio resource management, load balancing, and devices grouping for M2M communications, few of them has addressed the issue of admission control. In this paper, we propose a new admission control model for M2M communications, which classifies all M2M requests into delay-sensitive and delay-tolerant first, and then aggregates all delay-tolerant requests, aiming to reduce the number of requests from devices to base stations. An admission control algorithm based on this model is devised to prevent congestion and to improve the quality of services, and a network calculus based performance-analyzing technique is developed for this model. Both theoretical analyses and simulation results show that the proposed model is feasible and valid.

Internet traffic is experiencing an explosive growth, and online shopping is one of the significant drivers. However, alert network operators, unwilling to be dumb pipes, are making every effort to mine mass traffic with the help of Deep Packet Inspection (DPI) which is regarded as a big challenge especially for massive data when traditional methods and programming model are utilized. Hadoop provides an alternative approach with its strength in distributed storage and parallel computing. In this paper, a Hadoop based DPI system was reported, which was integrated with a web crawler. The system architecture and MapReduce models of packet analysis, web URL restoration were presented. As an example, live web traffic visiting the Tmall, the leading e-shopping giant in China, was specifically investigated using this system. Popularity of product, category and brand for a certain period was evaluated from page views of product. The detailed information of products was provided by the product information base built by the web crawler. This work explored the methodology of using Hadoop in DPI and presented valuable guidelines to develop such a system, which can be further used in analyzing other services and mining the value of network traffic by network operators.