Future low Earth orbit (LEO) mega-constellations will feature large-scale, dynamic, and multi-layer network topologies, posing new challenges for efficient routing. Traditional shortest-path algorithms, originally designed for two-dimensional planar networks, are inadequate for the three-dimensional, time-varying structure of LEO networks. The deployment of laser inter-satellite links (LISLs), while extending communication reach, significantly increases node density and exacerbates computational complexity. To address these issues, we propose Dynamic topology and Bidirectional Search (DBS), a novel algorithm tailored for routing in LISL-enabled multi-layer LEO mega-constellations. DBS integrates a bidirectional search strategy with customized cost functions for effective search-space pruning, substantially reducing computational overhead while preserving path optimality. We develop three DBS variants: DBS-OP for optimal routing, DBS-FS for fast routing, and DBS-RC for rapid path recovery. Theoretical analysis and extensive simulations demonstrate that DBS-OP achieves optimal paths with a significantly smaller search space than conventional methods, DBS-FS delivers near-optimal performance with accelerated execution, and DBS-RC ensures robust recovery under diverse failure scenarios. Overall, DBS provides an efficient, scalable, and adaptable routing solution for future large-scale LEO networks.

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication.

Virtual reality (VR) imaging is 360°, which requires a large bandwidth for video transmission. To address this challenge, tile-based streaming has been proposed to deliver only the focused part of the video instead of the entire one. However, the impact of cybersickness, akin to motion sickness, on tile selection in VR has not been explored. In this paper, we investigate Multi-user Tile Streaming with Cybersickness Control (MTSCC) in an adaptive 360$^\circ$video streaming system with multicast and cybersickness alleviation. We propose a novel$m^{2}$-competitive online algorithm that utilizes Individual Sickness Indicator (ISI) and Bitrate Restriction Indicator (BRI) to evaluate user cybersickness tendency and network bandwidth efficiency. Moreover, we introduce the Video Loss Indicator (VLI) and Quality Variance Indicator (QVI) to assess video quality loss and quality difference between tiles. We also propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward (video quality) and cost (cybersickness) estimation. The algorithm learns the weighting factor of each user's cost to slow down cybersickness accumulation for users with high cybersickness tendencies. We prove that the algorithm converges to an optimal solution over time. According to simulation with real network settings, our proposed algorithms outperform baselines in terms of video quality and cybersickness accumulation.

Virtual reality (VR) imaging, with its 360° perspective, poses bandwidth challenges for video transmission. To solve the challenge, tile-based streaming delivers only the viewed portion of the video rather than the whole video. However, the influence of VR sickness and social relations on tile quality assignment has not been fully investigated. In this paper, we formulate Tile and Handover Management regarding VR sickness and Social relation (THMVS) in an adaptive 360° video streaming system for mobile users, along with VR sickness alleviation. We propose a multi-armed bandit (MAB) algorithm with confidence bound-based reward and cost estimation to evaluate video quality and VR sickness. Our algorithm dynamically adjusts the tile quality and weighting factor of each user’s VR sickness increment according to user social relation, decelerating the VR sickness accumulation of susceptible individuals. Simulation results manifest that our algorithm outperforms existing video streaming algorithms regarding video quality and VR sickness accumulation.

In this paper, we explore the problem of minimizing the uplink and downlink age of information (AoI) in non-orthogonal multiple access (NOMA)-based low Earth orbit (LEO) networks for Internet of Things (loT) devices. The objective is to effectively manage the freshness of information across loT devices while maintaining a fair power allocation over time. We propose two novel AoI models that accurately capture the freshness of information for devices during both uplink and downlink transmissions, accounting for factors such as propagation delay, satellite handover delay, and inter-satellite link transmission time. To minimize the time-average AoI, we propose an energy-aware AoI (EA-AoI) algorithm that combines a deep reinforcement learning (DRL)-based scheduling approach with a low-complexity power allocation scheme. Simulation results demonstrate that the proposed EA-AoI scheduling algorithm outperforms successive interference cancellation (SIC)-based scheduling, AoI-greedy scheduling, and random scheduling approaches in terms of lower AoI values in the uplink and downlink directions through both simulated and real-world satellite trajectories. Furthermore, the algorithm ensures that the long-term average allocated power remains within a predefined threshold, thus striking an optimal balance between AoI and power consumption for NOMA-based LEO satellite networks.

Multi-view videos (MVV) have emerged to provide users with immersively interactive experiences with 3D multimedia content. Compared with traditional 2D videos, MVV offers multiple view angles to avoid generating occluded regions from a single viewpoint and allows users to receive different view angles, which consume much higher bandwidth. In this paper, we leverage multicast and view synthesis to effectively reduce the number of transmitted views and total bandwidth consumption in software-defined content delivery networks (SD-CDN). By exploiting the SDN architecture, SD-CDN can optimize traffic engineering and the selection of multi-view sources to serve users. We formulate a new optimization problem, (), prove the NP-hardness, and design an online algorithm with the ideas of View Popularity Cost Ratio, View Watching Possibility, and synthesis tree, to achieve the tightest competitive ratio. The experiment on real networks and implementation in an experimental SDN manifest that the proposed algorithm outperforms state-of-the-art algorithms regarding the total cost, bandwidth consumption, synthesis quality, and link and node utilization.

The virtual reality (VR) market is expected to reach 202.7 billion dollars by 2028, at a compound annual growth rate of 24.74% over the forecast period 2023–2028. It motivates innovative VR services in touring, E-commerce, and social activities, and effective VR video streaming becomes essential. However, VR services are envisaged to consume a large amount of bandwidth, but current research primarily focuses on multimedia streaming for each individual user without considering the opportunity of view synthesis for multicast to reduce wireless resource consumption further. In this article, we formulate a new optimization problem VR Content Sharing and Multicasting (VCSM) and prove the NP-hardness. Then, we propose an approximation algorithm, named Efficient View Synthesis and Multicasting (EVSM), to select multicast views and their Modulation and Coding Schemes (MCS) for wireless VR services. Afterward, we extend EVSM to support dynamic user behaviors and increase scalability with distributed mobile edge computing. We also explore the intrinsic properties of view selections to find the optimal solution for regular user deployment. Experiment results show that EVSM can effectively reduce bandwidth consumption for VR services by more than 50$\%$.

Low Earth Orbit (LEO) satellite networks are expected to enable global connectivity for next-generation communications. To provide space-centric solutions, the limited coverage time and limited resources of LEO satellites pose challenges to maintaining service continuity and ensuring low latency for users. Furthermore, LEO satellites rely on solar panels to obtain energy, so a balance needs to be struck between energy consumption and service provision for satellite mobile edge computing. In this paper, we aim to achieve space-centric computational task offloading in LEO satellite networks. The goal is to minimize end-to-end task offloading latency while considering the constraints posed by the limited onboard computing, storage, and energy resources in constantly moving LEO satellites. To achieve this, we formulate a joint problem of service migration and power control in energy-harvesting LEO satellite networks. The problem is then converted into a Markov decision process (MDP) and solved with SpaceEdge, a novel algorithm based on Deep Reinforcement Learning (DRL). SpaceEdge offers supports for both centralized learning and multi-agent learning. Simulation results show that SpaceEdge, particularly the multi-agent model, outperforms existing solutions, demonstrating its effectiveness in deploying space-centric task offloading services in LEO satellite networks.

Age of Information (AoI) is a critical performance metric for measuring information freshness in task-oriented Internet of Things (IoT) applications. In this work, we investigate how AoI impacts task-oriented multicasting in multi-cell Non-orthogonal Multiple Access (NOMA) networks, while considering the effects of inter-cell interference and multicast group size. Our findings reveal that larger group sizes lead to increased instability in AoI, but lower inter-cell interference; while smaller group sizes result in higher inter-cell interference but a smaller increase in AoI when transmission fails. To balance inter-cell interference and group size, we propose a novel AoI-aware inter-cell interference mitigation (AIM) solution, which dynamically adjusts multicast group size, schedules multicast groups for transmission, and allocates transmit power to multicast groups. We derive the optimality for achieving the lowest AoI of the AIM scheduling strategy and the upper bound of the number of scheduling groups. Our simulation results show that AIM ensures more successful transmissions and low weighted sum AoI through grouping and AoI-aware inter-cell interference mitigation. We also show that our AoI-aware grouping scheme outperforms random grouping and fixed grouping, as well as evolutionary-based genetic algorithm grouping, by modeling the grouping problem as a multi-armed bandit problem and solving it using the ϵ-greedy method. To the best of our knowledge, this is the first study to examine the AoI problem of task-oriented IoT applications in multi-cell multicast NOMA networks.

This study aims to enable space-centric adaptive video streaming through low earth orbit (LEO) satellites. Nowadays, satellite TV is sent in a broadcasting manner via geostationary orbit (GSO) satellites. However, the long distance between GSO and the Earth results in long latency, making GSO satellites less likely to provide livecast streaming services. To solve this problem, LEO satellites, which are much closer to the Earth, can relay traffic from video servers to remote users in high capacity and low latency. However, the orbiting characteristic of LEO satellites introduces new challenges such as frequent handover and fluctuating capacity that do not occur in GSO satellites. In this paper, we address the video streaming problem in LEO satellite networks, concerning adaptive bitrate, relay satellite selection and super-resolution. A novel solution called SkyTube, which is a Policy Proximal Optimization (PPO) based Deep Reinforcement Learning (DRL) method, is proposed to maximize the Quality of Experience (QoE) of the streaming user. The goal for our design is to formulate video streaming in LEO satellite networks into a resource allocation problem, thus the solution is not restricted to any special type of LEO satellite constellations. Simulation results show that SkyTube outperforms other baselines in terms of the cumulative QoE per orbit period. We also show the great performance and high convergence speed of SkyTube in all cases.

In this paper, we study the problem of dependent task offloading in mobile edge computing. Applications running on mobile devices require computing, but the computing resources of the mobile device are too few to meet the demand. To solve this problem, mobile devices can offload applications to nearby edge nodes, which are devices equipped with computing resources, for execution. Typically, each application can be divided into a set of dependent tasks whose execution dependencies form a directed acyclic graph (DAG). How to optimally offload these dependent tasks to resource-constrained edge nodes with the least application completion time (defined as the makespan) is a challenge. In this paper, we address this problem and propose a novel solution called GRL, which is a Deep Reinforcement Learning (DRL) model augmented by Graph Convolutional Networks (GCN). The rationale for our design is to utilize GCN to describe task dependencies and transform the problem of dependent task offloading into a node classification problem, thus perfectly handling non-Euclidean data. Simulation results show that the GRL model outperforms other algorithms in terms of the makespan of the application. It performs very well and converges quickly on various DAG structures of applications, even on real-world datasets.

Previous research on SDN multicast traffic engineering mainly focused on intra-domain optimization. However, the main traffic on the Internet is inter-domain, and the selection of border nodes and sharing of network information between domains are usually distributed but ignored in previous works. In this article, we explore multi-domain online distributed multicast traffic engineering (MODMTE). To effectively solve MODMTE, we first prove that MODMTE is inapproximable within$|D_{\max }|$, which indicates that it is impossible to find any algorithm with a ratio better than$|D_{\max }|$for MODMTE, and$|D_{\max }|$is the maximum number of destinations for a multicast tree. Then, we design a$|D_{\max }|$-competitive distributed algorithm with the ideas of Domain Tree, Dual Candidate Forest Construction, and Forest Rerouting to achieve the tightest performance bound for MODMTE. Experiments on a real SDN with YouTube traffic manifest that the proposed distributed algorithm can reduce more than 30% of the total cost of bandwidth consumption and rule updates for multicast tree rerouting compared with the state-of-the-art algorithms.

While mobile edge computing (MEC) potentially supports the stringent latency requirements for Virtual Reality (VR), previous research only considers minimizing the latency of transmitting required data and ignores the latency consistency of a group of friends in social VR. In this paper, we leverage the human motion prediction to eliminate the inconsistency among users in a group while ensuring the low latency interaction between friends with the aid of MEC. Moreover, to capture the accurate users’ interaction behaviors, it is critical to jointly predict the motions of users with spatially close proximity in VR. Accordingly, we formulate a new problem, named Group Motion Prediction for Social VR problem (GMSV), with group consistency requirement and the objective of prediction costs minimization on the MEC server. Then, we prove that GMSV is NP-Hard and design a new algorithm, named Social-Aware Latency Synchronization for Remote Users (SLSR), to select an appropriate set of remote users in social VR for motion prediction, with the ideas of generating a partial order set of remote subgroups and extracting the remote subgroups with high social interaction utilities. Simulation results show that SLSR can effectively increase latency consistency by more than 50% compared with the baseline schemes.

Remote working and virtual socializing are becoming the norm. This trend evokes the growth of virtual reality (VR). Users can explore virtual environments (VEs) with the most intuitive approach: walking. In contrast to traditional static VR experience, redirected walking (RW) enables users to walk in VEs larger than the physical space. Recent RW works aim to deliver an obstacle-free VR experience. However, they ignore the user’s cybersickness and received signal strength, which degrades the VR experience. In this paper, we investigate Path Planning with Cybersickness and IRS Control (PPCIC) in an indoor RW system with cybersickness and IRS control. We proposed an algorithm with Cybersickness Indicator (CI) and SINR Indicator (SI) to evaluate the cybersickness and received signal strength of each path. Simulation results show that our algorithm outperforms the baselines regarding the total cost, cybersickness, and received signal strength.

In 5G, eMBB services are defined to support high data rate, while URLLC services focus on low latency and high reliability. Multiplexing these two services on the same wireless radio frequency leads to a challenging radio resource allocation problem due to their heterogeneous requirements. In this paper, we formulate this problem as two non-linear programming non-convex optimization subproblems, aiming to maximize the average data rate of all eMBB services while satisfying the delay constraint of each URLLC service. We propose an event-driven deep reinforcement learning (DRL) based resource allocation mechanism (EDRL-RAM), which includes two schedulers: eMBB scheduler and URLLC scheduler to achieve long-term optimization of eMBB and URLLC performance. The eMBB scheduler will intelligently allocate resource for each incoming eMBB event, and the URLLC scheduler will intelligently distribute the incoming URLLC event during the ongoing transmissions of eMBB services. The proposed EDRL-RAM makes full use of four different DRL techniques to deal with stochastic event arrivals and network conditions, namely Policy Gradient (PG), Deep Q-learning Network (DQN), Advantage Actor Critic (A2C), and Deep Deterministic Policy Gradient (DDPG), in both eMBB and URLLC schedulers. The simulation results show that in our proposed EDRL-RAM, the order of data rate and delay performance is DDPG, A2C, DQN, and PG. The data rate and delay performance of the proposed EDRL-RAM utilizing any of the four DRL techniques are better than SAFE-TS, which is the best available related work.

Due to the emergence of mobile 3D devices, multi-view 3D videos are expected to play increasingly important roles in providing immersively interactive experiences to users. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires a larger storage space and bandwidth consumption. Nevertheless, efficient caching for multi-view 3D videos in a mobile proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D videos. We then formulate a new cache management problem, named Adaptive View Selection and Cache Operation (AVSCO), and find the optimal policy based on Markov Decision Process (MDP). Afterward, we propose an online algorithm with a guaranteed competitive ratio to support human visual continuity in AVSCO. Then, we devise an approximate MDP to accelerate the computation of MDP by aggregating similar states to reduce the state space. Simulation and prototype implementation results manifest that the proposed algorithms can significantly improve the cache hit rate and reduce the bandwidth consumption for the remote access compared with the existing cache replacement algorithms.

Fog computing is promising for Industrial Internet of Things (IIoT) which are typically large-scale, time-critical, and geo-distributed. However, it needs to address challenges such as scalability and heterogeneity in application provisioning with diverse and strict Quality-of-Service (QoS) requirements from IIoT devices. In this paper, a fog service orchestrator, Q-FSO, is proposed for IIoT application provisioning in which request satisfaction is maximized. With a two-level QoS model that considers key performance metrics for IIoT, including availability, reliability, response time, and cost, Q-FSO can realize large-scale service orchestration by exploiting the advantages of fog computing. Further with a harmonious local QoS assignment model, Q-FSO solves the workflow construction problem by multiple independent local optimization tasks, one for each stage, for the concurrent requests. Each local optimization task can be reduced from the multiple multidimensional knapsack (MMKP) problem. We then propose two practical MMKP heuristic algorithms, namely, Incremental Similarity Matching (ISM) and Greedy Multiple Matching (GMM), to tackle the local QoS assignment problem with polynomial time complexity. The scalability issue in orchestration is then handled by a decentralized workflow construction protocol. The simulation results validate their applicability, demonstrating that Q-FSO is indeed an efficient and effective IIoT application provision mechanism with significant performance benefits in terms of service processing throughput, resource utilization, and speed of service orchestration.

In contrast to traditional videos, the imaging in virtual reality (VR) is 360°, and it consumes larger bandwidth to transmit video contents. To reduce bandwidth consumption, tile-based streaming has been proposed to deliver the focused part of the video, instead of the whole one. On the other hand, the techniques to alleviate cybersickness, which is akin to motion sickness and happens when using digital displays, have not been jointly explored with the tile selection in VR. In this paper, we investigate Tile Selection with Cybersickness Control (TSCC) in an adaptive 360° video streaming system with cybersickness alleviation. We propose an$m$-competitive online algorithm with Cybersickness Indicator (CI) and Video Loss Indicator (VLI) to evaluate instant cybersickness and the total loss of video quality. Moreover, the algorithm exploits Sickness Migration Indicator (SMI) to evaluate the cybersickness accumulated over time and the increase of optical flow to improve the tile quality assignment. Simulations with a real network dataset show that our algorithm outperforms the baselines regarding video quality and cybersickness accumulation.

In this paper, we study fine-grained offloading for multi-access edge computing (MEC) in 5G. Existing works for computation offloading is on a per-task basis and do not take into account the execution order among tasks in one application. Fine-grained offloading, on the other hand, considers the task structure of an application upon making offloading decision and may only offload computation-hungry tasks to the MEC, thus making better use of system resource. To solve the problem, we propose an online solution based on Actor-Critic Federated Learning, called AC-Federate. In AC-Federate, we consider a multi-MEC network in which each edge node trains a model-free advantage Actor-Critic (AC) model based on local data. The AC model of each edge node jointly optimizes the continuous actions (i.e., radio and computing resource allocations) and the discrete action (i.e., offloading decision), and trains the model with a weighted loss function. To further improve the inference accuracy of the AC model, each edge node uploads the gradients of its actor and critic neural networks to a central controller in an asynchronous manner. The central controller then ensembles the collected gradients from different edge nodes and updates all edge nodes with the integrated network parameters. Simulation results show that the proposed AC-Federate outperforms DDPG and others in terms of delay, energy consumption, and mixed consideration of delay and energy consumption performance even when the number of UEs is very large.

Cloud data centers have become a popular infrastructure to host diversified application services for tenants. To provide agility and elasticity in resource usage for cloud services, the virtual data center (VDC) is proposed to allocate both virtual machines (VM) and network bandwidth. However, at cloud scale, hardware (e.g., link, server, and switch) failures are inevitable, which may lead to degradation in service performance. To address this challenge, we study the survivable virtual data center allocation problem (SVAP), which aims at allocating survivable virtual data center (SVDC) to each tenant to guarantee resource demands will always be satisfied even after failures. Our objective is to minimize the total bandwidth consumption in order to accommodate more SVDCs. We prove that SVAP is NP-hard and design the Collocation-Aware survivable VM placement and Link Mapping algorithm (CALM). CALM solves the problem in two stages, i.e., VM placement (VMP) and virtual link mapping (VLM). We further find that without an appropriate VMP strategy, VLM cannot lead to the minimum network resource usage. Therefore, we propose a polynomial-time algorithm called collocation-aware survivable placement (CASP) for VMP. For the VLM stage, we formulate a linear programming model to map flows onto the data center network in order to ensure survivability under switch failures. We evaluate the performance via simulations and show that CALM could save up to 42 percent network resource compared to the baseline algorithm. We further show that CALM uses only additional 13 percent network resource to guarantee survivability as compared to a typical VDC strategy.

Energy saving in cellular systems has attracted the extensive attention of various studies due to ever-deteriorating global warming. In addition to network greenness, various emerging mobile applications (e.g., mixed reality and automated vehicles) further necessitate timely service provision. Recently, the age of information (AoI) has been regarded as a promising performance metric for quantifying the freshness (i.e., timeliness) of information updates in communication systems. Despite the existing works devoted to energy saving in cellular systems, how to leverage information freshness to ensure timely information updates while achieving network greenness is rarely investigated. In this paper, we investigate the problem of freshness-aware energy saving in cellular systems, where active base stations (BSs) can cooperatively update information to target devices (TDs). To address this problem, we formulate a mixed-integer nonlinear program (MINLP) to minimize average power consumption. Due to its intractability, we propose decomposing the MINLP into two subproblems by means of constraint reinterpretation and spatiotemporal decoupling. Then, we propose a two-stage solution that leverages LP techniques to sequentially determine update scheduling and BS activeness. Our simulation results show that the proposed solution can adequately perform update scheduling and BS activeness control, thereby effectively saving energy for cellular systems under various parameter settings.

With the emergence of innovative applications for Virtual Reality (VR) in touring, E-commerce, and social activities, high-quality VR video streaming becomes essential. To support numerous wireless VR users, this paper aims to leverage video synthesis techniques to effectively reduce the multicast bandwidth consumption. It synthesizes the view in the video for a user from the one of a nearby user with similar Field of View (FoV), under the virtual distance and view angle constraints. We first formulate a new optimization problem, named VR Content Sharing and Multicasting (VCSM), and prove the NP-hardness. Then, we propose View Sharing Relation Graph (VSRG) to model the synthesis relation between each pair of views. We then design a new algorithm, named Bandwidth-Efficient Multicast with Synthesis (BEMS) to select multicast views and the corresponding MCS in wireless networks. Simulation results show that BEMS can effectively reduce bandwidth consumption by more than 50% compared with state-of-the-art wireless transmission schemes.

Multi-access Edge Computing (MEC) is promising to handle computation-intensive and latency-sensitive applications for 5G and beyond. Users can benefit from task offloading via wireless channels to MEC servers deployed at the nearby network edge. However, the radio resource is scarce and the computing resource in MEC is limited as compared to the remote cloud. Upon making an offloading decision, it is also important to efficiently allocate radio resource and MEC computing resource to ensure better service for the upload tasks. In this paper, we target the long-term delay and energy consumption performance in a multi-user system, and design an online solution based on Deep Reinforcement Learning (DRL) to deal with time-varying user requests and wireless channel conditions. To obtain better convergence property, we propose a new Actor-Critic model, called Discrete And Continuous Actor-Critic (DAC), to jointly optimize the continuous actions (i.e., radio resource allocation and computing resource allocation) and the discrete action (i.e., offloading decisions), and train the model iteratively with a weighted loss function. Our simulation results show that DAC outperforms existing solutions based on DDPG, DQN, and others, in terms of convergence speed, delay, and energy performance.

Multi-access edge computing (MEC) has emerged as a promising platform to provide user equipment (UEs) with timely computational services through the deployed edge servers. Typically, the size of an uplink task data (e.g., images or videos) required for processing is more pronounced than that of a downlink task result, and hence MEC offloading (MECO) plays a decisive role in the efficiency of MEC systems. In the light of an unprecedented growth of UEs in next-generation mobile networks, the reception of uplink signals at base stations (BSs) can be corrupted due to potential inter-user interference. To address this issue, coordinated multi-point (CoMP) reception which enables BSs to cooperatively receive uplink signals has evolved as an effective approach to enhance the received signal qualities. In this paper, we investigate a resource allocation problem for MECO with CoMP reception and formulate it as a mixed-integer non-linear program (MINLP). To solve this problem, we leverage the concept of interference graphs to characterize uplink inter-user interference, based on which we propose a resource allocation algorithm that consists of three phases: 1) computing resource allocation, 2) subcarrier allocation and cell clustering, and 3) subcarrier reuse and cell re-clustering. The simulation results show that our proposed solution can effectively enhance the delay performance of MECO through CoMP reception as compared with existing solution approaches under various system settings.

With the emergence of virtual reality (VR) like holographic telepresence, it is envisaged that the current networks may be overwhelmed due to the higher bandwidth demand to support high-resolution videos. On the other hand, Camera Blending Field (CBF) has been proposed to restore the occluded regions of cameras to enhance the user’s Quality of Experience (QoE) in VR. In this paper, we explore the source selection to maximize the user’s QoE under the network capacity and 3D reconstruction constraints. We first formulate a new optimization problem, named Source Selection for Real-time Telepresence with Occlusion (SSRTO), with the objective of QoE maximization. Then, we prove the NP-hardness and propose a new algorithm, Maximum Quality of Complete Model (MQCM), to maximize the user’s QoE by examining the proximity of cameras on the built Angle Directed Graph (ADG) and Neighbor Directed Graph (NDG) for reconstructing the complete 3D model. Simulation results show that MQCM can effectively improve the user’s QoE by more than 100% compared with the baseline schemes.

The incorporation of Virtual Network Function (VNF)/Service Function Chaining (SFC) and Mobile Edge Computing (MEC) allows 5G networks to deliver a variety of services and applications in a more flexible manner. However, without inter-MEC handoffs for the VNFs of the corresponding SFC, user mobility may affect user satisfaction of the service due to unacceptably long service delay. To tackle this problem, one has to determine, upon a handoff, which VNFs of the SFC to be migrated, to which MEC server(s), and with how much resource of each MEC server to be allocated to the user so that the service delay constraint of the SFC will not be violated. This problem is referred to as the mobile SFC embedding problem, which is NP-Hard, and is formulated via integer programming to minimize service interruption for mobile users. We then propose an on-line algorithm called Follow-Me Chain, which includes two key components: SFC placement and SFC migration, to solve this problem. The simulation results show that the proposed Follow-Me Chain outperforms existing solutions in terms of user satisfaction under the same number of users accommodated in the system, demonstrating that FMC is indeed an efficient and effective solution for mobile SFC embedding problem in 5G networks with MEC.

Energy saving in cellular systems is increasingly important due to the ever- deteriorating global warming. Heterogeneous networks (HetNets) can attain energy savings thanks to the lower operational and transmit power consumption of small base stations (BSs). To address inter-cell interference problem yet achieving network energy conservation, green multicell clustering facilitating BS sleeping and coordinated multipoint (CoMP) clustering paves a way toward future green HetNets. To further alleviate the backhaul power consumption induced by content requests from users, caching popular contents at BSs in a shareable manner is regarded as a viable solution. In this paper, we investigate the problem of green multicell clustering in HetNets with shareable caching (RELISH), and show its NP-hardness. By observing that BS sleeping plays a pivoting role in the RELISH problem, we propose the clustering- then-caching strategy to decompose the RELISH problem, and then design the dual- ascending clustering algorithm followed by the zero-replica caching algorithm for solving the sub-problems. The simulation results demonstrate that our proposed solution is effective in reducing total power consumption, and we also show how the power savings vary with system parameters.

In this paper, we propose a resource management framework called StarCube, which guarantees non-blocking resource allocation and topology-preserving reallocation for fat-tree based multi-tenant cloud data centers. With StarCube, each cloud service is allocated an isolated non-blocking virtual network topology, and the topology provisioned to each service is guaranteed logically unchanged during and after virtual machine reallocation. This resource management problem is formulated and proved to be NP-complete. To achieve high resource efficiency in acceptable time, we propose a cost-effective algorithm with polynomial-time complexity based on StarCube for on-demand resource allocation and reallocation. We demonstrate via extensive simulations that the server resources in StarCube-based cloud data centers can be nearly fully utilized with negligible reallocation cost. The results also show that StarCube supports a large variety of service provisioning feasibly and efficiently for cloud data centers of various scales and with dynamic demands. To the best of our knowledge, StarCube is the first solution to allocating and reallocating cloud services for fat-tree networks with guarantee on non-blocking properties.

The matrix factorization (MF) technique has been widely adopted for solving the rating prediction problem in recommender systems. The MF technique utilizes the latent factor model to obtain static user preferences (user latent vectors) and item characteristics (item latent vectors) based on historical rating data. However, in the real world, user preferences are not static but full of dynamics. Though there are several previous works that addressed this time-varying issue of user preferences, it seems (to the best of our knowledge) that none of them are specifically designed for tracking concept drift in individual user preferences. Motivated by this, we develop a temporal MF approach for tracking concept drift in each individual user latent vector. There are two key innovative steps in our approach: 1) we develop a modified stochastic gradient descent method to learn an individual user latent vector at each time step and 2) by Lasso regression, we learn a linear model for the transition of the individual user latent vectors. We test our method on a synthetic data set and several real data sets. In comparison with the original MF, our experimental results show that our temporal method is able to achieve lower root mean square errors (RMSEs) for both the synthetic and real data sets. One interesting finding is that the performance gain in RMSE is mostly from those users who indeed have concept drift in their user latent vectors at the time of prediction. In particular, for the synthetic data set and the Ciao data set, there are quite a few users with that property and the performance gains for these two data sets are roughly 20% and 5%, respectively.

With the recent emergence of naked-eye 3D mobile devices and various 3D-enabled laptops, service providers now afford the opportunity to provide mobile 3D video streaming in LTE-Advanced networks. Differing from traditional single-view 3D videos, multi-view 3D videos allow users to choose preferred view angles and thus are promising for new applications, such as free-viewpoint television (FTV). Nevertheless, enabling multi-view 3D video services may overwhelm the network resource when transmitting all views of every video. Fortunately, Depth-Image-Based Rendering (DIBR) allows each mobile client to synthesize the desired view from a nearby left view and right view, so that not all views of a video are necessarily transmitted. A new challenge with DIBR, however, is to carefully choose the transmitted views to limit the video distortion and minimize the bandwidth consumption. In this paper, therefore, we first formulate a new optimization problem, called View and MCS Selection (VMS) Problem, to minimize the bandwidth consumption for multi-view 3D video multicast in LTE networks. An algorithm, called View and MCS Aggregation (VMAG) is proposed to find the optimal solution to VMS. For Carrier Aggregation (CA) in LTE-Advanced networks, we formulate a new View, MCS and Carrier Selection (VMCS) Problem and prove that the problem is NP-Hard. We first design a dynamic programming algorithm, called the View Assignment with MCS and Carrier (VAMC) algorithm, to find the optimal solution for small instances. We then propose the View and MCS Aggregation with Carrier (VMAGC) algorithm based on VMAG to effectively find the near-optimal solution to VMCS. The simulation results show that bandwidth consumption can be effectively reduced by over 30 percent in VMS and VMCS.

Recent research trends exhibit a growing imbalance between the demands of tenants' software applications and the provisioning of hardware resources. Misalignment of demand and supply gradually hinders workloads from being efficiently mapped to fixed-sized server nodes in traditional data centers. The incurred resource holes not only lower infrastructure utilization but also cripple the capability of a data center for hosting large-sized workloads. This deficiency motivates the development of a new rack-wide architecture referred to as the composable system. The composable system transforms traditional server racks of static capacity into a dynamic compute platform. Specifically, this novel architecture aims to link up all compute components that are traditionally distributed on traditional server boards, such as central processing unit (CPU), random access memory (RAM), storage devices, and other application-specific processors. By doing so, a logically giant compute platform is created and this platform is more resistant against the variety of workload demands by breaking the resource boundaries among traditional server boards. In this paper, we introduce the concepts of this reconfigurable architecture and design a framework of the composable system for cloud data centers. We then develop mathematical models to describe the resource usage patterns on this platform and enumerate some types of workloads that commonly appear in data centers. From the simulations, we show that the composable system sustains nearly up to 1.6 times stronger workload intensity than that of traditional systems and it is insensitive to the distribution of workload demands. This demonstrates that this composable system is indeed an effective solution to support cloud data center services.

Optimal resource provisioning is essential for scalable big data analytics. However, it has been difficult to accurately forecast the resource requirements before the actual deployment of these applications as their resource requirements are heavily application and data dependent. This paper identifies the existence of effective memory resource requirements for most of the big data analytic applications running inside JVMs in distributed Spark environments. Provisioning memory less than the effective memory requirement may result in rapid deterioration of the application execution in terms of its total execution time. A machine learning-based prediction model is proposed in this paper to forecast the effective memory requirement of an application given its service level agreement. This model captures the memory consumption behavior of big data applications and the dynamics of memory utilization in a distributed cluster environment. With an accurate prediction of the effective memory requirement, it is shown that up to 60 percent savings of the memory resource is feasible if an execution time penalty of 10 percent is acceptable. The accuracy of the model is evaluated on a physical Spark cluster with 128 cores and 1TB of total memory. The experiment results show that the proposed solution can predict the minimum required memory size for given acceptable delays with high accuracy, even if the behavior of target applications is unknown during the training of the model.

Downlink inter-cell interference is a major limiting factor in current cellular systems. Several trends exacerbate the interference problem: cells become denser, more heterogeneous, and are more randomly deployed. To tackle this problem, there are many pure network-side solutions proposed, e.g., eICIC and feICIC in 3GPP LTE. However, these solutions are less spectrally efficient because the interferer usually needs to mute or reduce power to keep its interference power to victims below a tolerable level. In this paper, we consider an alternative solution in which the user equipment may perform interference cancellation (IC). In theory, this is a more spectrally efficient scheme as compared with the pure network-side solutions. In practice, however, to fully exploit the benefit of IC, the network should adopt some IC-aware scheduling rules. We propose a multi-cell joint scheduling, joint rate selection and power allocation problem in an IC-enabled network, trying to maximize the network utility. To make our solutions practical and readily deployable, we consider different settings. We explore both theoretically optimal IC scheme and the more practical symbol-level IC. On the other hand, we propose both dynamic and semi-static solutions, and compare them. Our simulation results show that while the average throughput increases by only around 10%, cell edge users can gain more than 50%.

The introduction of small cells has displayed its energy-saving potentials in heterogeneous networks (HetNets) for the low operational and transmit power consumptions. To cope with the severity of inter-cell interference induced by the deployed small cells, existing research has been investigating base station (BS) sleeping incorporated with coordinated multipoint (CoMP) transmissions for the greenness of HetNets. Unfortunately, the fundamentals of multicell sleeping control and transmit power adaptation (MST) in green HetNets are in essence an NP-hard problem, which motivates us to find approximate solutions with provable performance guarantees. In this paper, we formulate the MST problem as a mixed integer linear program (MILP) and show its NP-hardness. By applying linear programming (LP) relaxation to the MST problem, we propose the progressive sleeping control with LP-based transmit power adaptation (PSLA) algorithm. We further prove that the achieved total power consumption can be upper-bounded, and show that the tightness of the upper bounds hinges on the factors of user satisfiability and network heterogeneity. Finally, the simulation results demonstrate the energy-saving performance of our proposed solution, as well as the impacts of the number of users and small cells in the network.

Cloud radio access networks (C-RAN) has been regarded as a promising solution to the next generation communication system, but the massive fronthaul bandwidth required to aggregate baseband samples from remote radio head (RRH) to the baseband unit (BBU) pool has a significant impact on the performance of C-RAN. Existing baseband compression algorithms can hardly solve this problem. So, in this paper we consider a new flexible C-RAN architecture with two types of RRHs with different degrees of centralization in the network, namely, primitive RRH and RRH with Layer 1 functions. The objective is then to determine where and how many nodes of each type to be deployed in the target service region so that the deployment cost is minimized, under the condition that the fronthaul capacity is limited and the total traffic demand in the system is satisfied. We prove that the problem is NP-hard and propose an efficient algorithm with polynomial time complexity to solve the problem. The simulation results show that the proposed solution is indeed better than existing solutions and also adaptive to different types of traffic distributions and demands.

Dense deployments of small cells in heterogeneous networks (HetNets) massively increase spectral efficiency and capacity. Since ubiquitous fiber availability is prohibitively expensive, wireless backhaul technique via millimeter wave (mmWave) is regarded as a practical solution. Additionally, mmWave with huge under-utilized bandwidth is able to provide multi-Gbps data rate comparable to fiber lines. In this paper, we propose an mmWave hierarchical small cell base station backhauling (mw-HierBack) system to organize densely deployed base stations (BSs) in HetNet. A group of Super-BSs (S-BSs) are selected to minimize the resource cost on the gateways and to robustly relay backhaul traffic of the remaining BSs to the core network against any blockage or link failure. Under this network structure, we present a path protection strategy to balance backhaul traffic among S-BSs and to minimize the fluctuations incurred by rerouting. The simulation results show our solution can efficiently scale with the growth of BSs and properly route the backhaul traffic without overloading any one of the S-BSs.

Switching off base stations (BSs) is an effective and efficient energy-saving solution for green cellular networks. The previous works focus mainly on when to switch off BSs without sacrificing the traffic demands of current active users, and then enlarge the coverage of the stay-on cells to cover as many users as possible. Based on this objective, both constant power and transmission power of each BS become the major energy consumption sources. However, the transmission powers of enlarged cells, which have not been taken into account in previous research, are not negligible as compared to other energy consumption sources. To tackle this problem, we observe that the transmission power of one specific BS could be reduced via cooperation among two or more BSs, which is typically used to improve the throughput or enhance the spectrum efficiency in wireless systems. The challenges come mainly from how to jointly consider which BSs to switch off and how to cooperate among active-mode BSs. In this paper, we design energy-aware cooperation strategies that ensure that our system is energy-saving while satisfying user demands. To cope with sleep-mode BSs and perform cooperation among active BSs, we formulate this problem as a binary integer programming problem, and prove it is NP-hard. Based on our formulation, we derive a performance lower bound for this problem via Lagrangian Relaxation with search enumeration. Furthermore, we propose two heuristic algorithms accounting for the properties of energy savings and the constraints of bandwidth resources. The simulation results show that our algorithms outperform pure power control mechanisms that do not consider the transmission power and pure cooperation without power control in terms of the total consumed energy. We also observe that larger cooperative size does not imply a better strategy under different scenarios. Compared to the total consumed energy given that all BSs are turned on, our algorithms can save up to 60 percent of energy. This demonstrates that our methods are indeed efficient energy-saving cooperation strategies for green cellular networks.

The multichannel rendezvous problem in cognitive radio networks (CRNs) has been a hot research topic lately. One of the most challenging settings of the multichannel rendezvous problem is the oblivious rendezvous problem in heterogeneous CRNs, where: 1) there are no distinguishable roles of users; 2) users' clocks are not synchronized; 3) users may have different available channel sets; and 4) there is no universal labelling of the channels. Most existing works in the literature focus on achieving deterministic bounds for the maximum conditional time-to-rendezvous (MCTTR) and perform poorly (in comparison with the random algorithm) for the expected time-torendezvous (ETTR) due to the “stay” modes in these works. In this paper, we tackle the oblivious rendezvous problem by taking both MCTTR and ETTR into consideration. In order to have guaranteed rendezvous, we only make two assumptions: (A1) there is at least one common available channel and (A2) there is a unique ID for each user. We first propose a new class of strong symmetrization mappings to encode user IDs for speeding up the rendezvous process. Two efficient and yet simple encoding schemes are proposed by utilizing the C-transform and the existing 4B5B encoding. Based on the new class of strong symmetrization mappings, we propose the twoprime modular clock algorithm for the two-user rendezvous problem. The ETTR of our algorithm is almost the same as that of the random algorithm and its MCTTR is also comparable to the best existing bound. We also extend the two-prime modular clock algorithm for multiuser rendezvous by proposing the stick together algorithm and the spread out algorithm. One interesting finding for the multiuser rendezvous problem is that the spread out algorithm is not always better than the stick together algorithm as commonly claimed in the literature.

Due to the emergence of mobile 3D and VR devices, multi-view 3D videos are expected to play increasingly important roles shortly. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires a larger storage space. Nevertheless, efficient caching of multi-view 3D videos in a proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D. We then formulate a new cache replacement problem, named View Selection and Cache Operation (VSCO), and find the optimal policy based on Markov Decision Process. Also, we devise an efficient and effective algorithm to solve the problem. Simulation results manifest that the proposed algorithm can significantly improve the cache hit rate and reduce the total cost compared with the previous renowned cache replacement algorithms.

With the emergence of 3D mobile and VR devices, mobile 3D video services are becoming increasingly important for video service providers, such as Youtube and Netflix, while multi-view 3D videos have the potential to inspire a variety of innovative applications. However, enabling multi- view 3D video services may overwhelm WiFi networks when every view of a video are multicasted. In this paper, therefore, we propose to leverage depth-image-based rendering (DIBR) in multi-view 3D, which allows each mobile client to synthesize the desired view from nearby left and right views, in order to effectively reduce the bandwidth consumption. Moreover, when each client suffers from packet losses, retransmissions incur additional bandwidth consumption and excess delay, which in turn undermines the quality of experience in video applications. To address the above issue, we first discover the merit of view protection via DIBR for multi-view video multicast using a mathematical analysis and then design a new protocol, named Multi-View Group Management Protocol (MVGMP), to support the dynamic join and leave of users and the change of desired views. The simulation results demonstrate that our protocol effectively reduces bandwidth consumption and increases the probability for each client to successfully playback the desired views in a multi-view 3D video.

Heterogeneous cloud radio access networks (H-CRAN), which combines the benefits of cloud radio access radio networks (C-RAN) and heterogeneous networks (HetNets), is an emerging technology to achieve high data transmission rates in 5G cellular networks. In H-CRAN, to mitigate interference among macro BSs (known as high-power nodes (HPNs)) and remote radio heads (known as low-power nodes (LPNs)), Fractional Frequency Reuse (FFR) is used to enhance spectral efficiency in radio resource. However, FFR may incur inefficient resource utilization in OFDMA wireless networks. To solve this problem, in this paper, we explore how to enhance throughput within one cell based on the architecture of H-CRAN based on cooperative communications among HPN and LPNs. Specifically, we study how to form a cooperative cluster efficiently among LPNs for cross-tier cooperation, and propose a hierarchical cooperation strategy to improve system throughput in H-CRAN. We then show via simulations that the proposed hierarchical cooperation is truly an efficient resource allocation strategy in H-CRAN.

Ultra-dense network is considered a promising solution for high network capacity. It is favored for its scalability and cost effectiveness. However, when a user equipment (UE) is in a long discontinuous reception cycle and at high speeds, it may suffer from poor mobility performance and high power consumption. While handover algorithms have been widely studied, the impact of mobility measurements on mobility performance is largely ignored. In this paper, we investigate how to perform mobility measurements intelligently. We analyze the radio link failure rate and the timing of handover. We then propose a mechanism to adjust measurement frequency based on the analysis to minimize the power consumption. Our simulation results demonstrate the mechanism can save considerable amount of energy in ultra-dense networks.

Energy saving in cellular systems is increasingly important due to ever-deteriorating global warming. Heterogeneous networks (HetNets) composed of various tiers of cells can attain energy savings thanks to the lower operational and transmit power consumptions of small cells. To address the inter-cell interference problem yet achieving network energy conservation, multicell cooperation facilitating cooperative transmission (also known as coordinated multipoint or CoMP) and sleep mode operation paves a way toward future green HetNets. To further alleviate the induced backhaul energy consumption caused by cooperative transmission, content caching which proactively caches popular files at local storages is regarded as a viable solution. In this paper, we investigate how energy-aware multicell cooperation in HetNets with content caching (ENCORE) can be achieved. On proving that the ENCORE problem is decomposable into two sub-problems, we claim that the place-then-transmit strategy is optimal to the ENCORE problem. Then, we design algorithms for the sub-problems and prove that the total energy consumption achieved by the proposed solution is upper-bounded. Our simulation results demonstrate that the proposed solution outperforms various dynamic clustering approaches in terms of energy savings, and show the impacts of content popularity and cache size on the backhaul energy consumption.

In this paper, we consider the two-user multichannel rendezvous problem in a cognitive radio network (CRN) and derive tight lower bounds for maximum time-to-rendezvous (MTTR) and maximum conditional time-to-rendezvous (MCTTR) of various channel hopping (CH) schemes under a channel loading constraint. In the symmetric and synchronous setting, we propose a novel Cycle-Adjustable Channel Hopping (CACH) scheme to achieve the MTTR lower bound (when the channel loading is bounded above by 1/u with u being a prime power). Thus, the MTTR lower bound is tight and the CACH scheme is optimal in minimizing MTTR among all the symmetric and synchronous CH schemes under the same channel loading constraint. In the asymmetric setting, we show that the classical wait-for-mommy strategy can be used to achieve the MCTTR lower bound, and thus it is optimal. In the symmetric and asynchronous setting, we also show a hierarchical construction of an asynchronous CH sequence by using two smaller asynchronous CH sequences. To further understand the effect of channel loading to the other performance metrics in a CRN, we perform various computer simulations for various CH schemes. Our simulation results show that the average time-to-rendezvous of CACH is independent of the total number of channels, and it is also robust to the disturbance of primary users.

Concerns with global warming have prompted much research effort in energy-related issues for cellular systems. Heterogeneous networks (HetNets) incorporated with small cells save energy effectively due to the lower transmit and operational power consumptions of small cells. Multicell cooperation employing both cooperative transmission to tackle inter-cell interference and sleep mode operation to attain network energy conservation plays a decisive role in future green HetNets. Energy harvesting is another trend to achieve network energy savings. In this paper, we investigate how green multicell cooperation (GMC) can be achieved in HetNets facilitated with hybrid energy sources. Due to the lack of efficient solution approaches in coping with the spatial and temporal characteristics of the GMC problem simultaneously, we propose a greedy decomposition to solve the GMC problem via two sub-problems. We prove that the achieved grid energy consumption can be upper bounded, and observe that the tightness of the upper bounds hinges on two clustering-related factors, which may guide the scheduling design in future green HetNets. Our simulation results show that the proposed solution outperforms the existing approaches in terms of grid energy savings, and also demonstrate the cluster formation results and the dynamics of the clustering-related factors.

Depth-Image-Based Rendering (DIBR) is a popular technology and enables the viewers to watch multi-view 3D videos via TV or smart phone. In this paper, we propose a game-theoretical framework over a multi-view and multi-video network where one server delivers multiple multi-view 3D videos to multiple viewers. In a mobile network, any individual viewers can subscribe to a video with any viewing angle within a specified viewing range in 3D-enabled mobile handsets. To facilitate efficient resource allocation for multi-view 3D videos, we formulate a new resource allocation problem, named Price-based 3D Viewing Range Control problem (PVRC), as a Stackelberg game in Game Theory, in which the server and the viewers are modeled as the leader and the followers, respectively, to jointly consider the revenue of the server and the utilities of the viewers. We analyze the price setting problem by investigating the Stackelberg equilibrium between the server and viewers. Simulation results demonstrate that compared to popular uniform-price setting, non-uniform pricing is an effective approach for reducing the difference of utilities among the viewers, effectively controlling the viewing ranges and avoiding starvation of the network resources to only few viewers. Moreover, we also derive the social optimum in the problem, and the simulation results show that the derived Stackelberg equilibrium is close to the social optimum.

In user-item networks, the link prediction problem has received considerable attentions and has many applications (e.g., recommender systems, ranking item popularity) in recent years. Many previous works commonly fail to utilize the dynamic nature of the networks. This paper focuses on dealing with the temporal information and proposes an algorithm to cope with the link prediction problem on bipartite networks. We describe a temporal bipartite projection method that yields a projected item graph, called the temporal projection graph (TPG). Based on the TPG, we propose a scoring function called STEP (Score for TEmporal Prediction) for each user-item pair. STEP leverages the historical behaviors of individual users and the social aggregated behaviors learned from the TPG for the link prediction problem. Furthermore, we use TPG and PageRank to rank the popularity of items. To validate our algorithms, we perform various experiments by using the DBLP author-conference dataset, the Flickr dataset and the Delicious dataset. We show that our results of the link prediction problem for new links are substantially better than other temporal link prediction algorithms. We also find the item rankings generated by our approach match very well with that existed in the real world.

A heterogeneous network (HetNet), which consists of macro cells and small cells, is an effective tool to improve the system spectral efficiency by exploiting the benefit of spatial reuse. However, the benefit of HetNet is limited by the asymmetry of transmit power between macro base stations (BSs) and pico BSs. Traditional solutions lead to a tradeoff between received signal strength (RSS) and load balancing. To tackle this problem, we propose a novel mechanism with joint macro BS transmit power control and load-aware user association. As such, we can realize traffic offloading from the macro cell to pico cells in a two-tier HetNet and mitigate inter-cell interference (ICI) in the network. We formulate this problem as a nonlinear integer programming problem, and prove that it is NP-hard. We then propose a polynomial-time heuristic. The simulation results show that our algorithm is indeed an efficient mechanism with better network performance in terms of average throughput and fairness.

The increasing severity of the inter-cell interference problem due to universal frequency reuse has prompted many research studies on coordinated multipoint (CoMP) transmission. In addition to the enhancement of the received signal strength, CoMP transmission can be performed in an energy-efficient manner. However, existing works on CoMP transmission do not investigate the potentials of energy saving with the incorporation of renewable energy. In this paper, we formulate a renewable energy aware cluster formation (REAL) problem to minimize the energy consumption in electric grid, with the support of hybrid energy supply in each cell site. Due to the difficulties in solving the REAL problem, we propose to decompose it into two stages and design polynomial-time algorithms for the decomposed problems. In our simulation results, we show the achieved SINR by the harvested energy in cluster formation. Then, we demonstrate that our proposed solution can better utilize the harvested energy and effectively save the energy consumption in electric grid.

In this paper, we investigate how to utilize renewable energy harvested by small cells to increase network capacity. Specifically, we try to maximize the average capacity of a small cell with the harvested energy from the environments (e.g., solar and wind) with constraints on energy causality and battery capacity. With the coverage preserved by the umbrella cells, we investigate the potential of capacity improvement by dynamic on-off operations of small cells. We propose a heuristic polynomial-time near-optimal algorithm for joint power control and sleep-awake scheduling of this mixed-integer optimization problem. The capacity obtained by the proposed heuristic algorithm can approach the maximal capacity as long as the small cell can be equipped with a battery with large enough capacity. In the simulations, we demonstrate that our proposed algorithm can increase system capacity by 25%. We find that always attempting to keep a small cell in active state may not be always a good strategy for capacity maximization even when the optimal transmit power allocation is applied.

Establishing control channels in a cognitive radio network (CRN) is an important and challenging problem. To cope with the problem of control channel saturation and the problem of channel blocking by primary users, channel hopping (CH) schemes are commonly used in the literature for control channel establishment in CRNs. There are three metrics that are widely used for evaluating the performance of CH schemes: (i) degree of overlapping (the number of distinct rendezvous channels), (ii) worst case time-to-rendezvous (TTR), and (iii) system load. In this paper, we focus on the symmetric and synchronous setting and propose a novel Cycle-Adjustable Channel Hopping (CACH) scheme that outperforms several existing CH schemes, including SSCH and QCH, in terms of the three metrics. The key idea of CACH is to create an additional layer of logical channels on the top of physical channels so that the cycle of channel hopping sequences can be adjusted to optimize system performance. The mathematic tools for our scheme are based on the operations in Galois fields that are more general than the prime number modular arithmetic used in SSCH. We show that CACH is much more general than SSCH and it can achieve the maximum degree of overlapping while allowing the worst case TTR to be adjustable. It is also much better than QCH in terms of reducing system load while keeping the same degree of overlapping and the same worst case TTR. Our simulation results show that CACH outperforms several existing schemes in many other aspects, including throughput, and robustness to the disturbance of PUs.

A heterogeneous network (HetNet) with macro base stations (BSs) together with low power small cells, such as pico BSs and femto BSs, is a promising solution to enhance network capacity. In HetNet, the transmission range of pico BSs are limited due to the large transmission power difference between macro and pico BSs, and thus pico BSs are typically underutilized. Cell Range Expansion (CRE) is a way to increase the opportunity of the user equipment (UE) associations to pico BSs so that more traffic from the macro cell can be offloaded to the pico cells. However, the expansion region of pico UEs with unbalance downlink and uplink signal qualities suffer from bad downlink quality, and the macro UEs near the expansion region may also experience low uplink rates. Therefore, we propose a new scheme with rate-based CRE offsets in HetNet so that UEs can decide to their associations based on their traffic demands. We formulate this problem as a mixed integer programming problem and prove that it is NP-hard. We then propose a polynomial time heuristic algorithm to solve this problem. The simulation results show that our scheme indeed achieves good traffic offloading gain for HetNet.

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. An effective way for energy conservation of BSs is to switch BSs on/off according to the traffic profile. However, such operations may create coverage holes in the network. In this paper, we aim to minimize the total power consumption of the network by switching BSs on/off adaptively while maintaining the network coverage. We find that the BS activation problem for minimal network power consumption with full network coverage preservation is an NP-hard problem. To address the problem, we first derive the optimal cell size for minimizing BS power consumption per unit coverage area and propose a polynomial-time algorithm for energy-efficient BS activation. The simulation results show that our algorithm can approach the minimum network power consumption and adapt to network traffic load under non-uniform traffic load distributions. More importantly, we demonstrate that network densification with small cells for bursting throughput in hot spot areas can also be beneficial in saving network energy during the low traffic load period.

In this paper, we study the delay minimization problem for wireless data broadcasting in cognitive radio networks (CRN). In CRN, each secondary user (SU) has a set of available channels to communicate with each other but there may not be a common available channel set among all the SUs. Therefore, a simple way to shorten the broadcasting delay among them is to select a minimal set of available channels which cover all SUs in the network. However, this scheme may incur a fairness problem in resource sharing among SUs and also lower the system resource utilization. To tackle this problem, we assign channels to SUs for data broadcasting service in downlink sub-frames by jointly considering broadcast delay minimization and fair resource sharing. We formulate this problem as a nonlinear integer programming problem, and show that it is NP-hard by applying a reduction from the 3-dimensional matching problem. We then propose an enumerative algorithm to find an optimal solution and design a greedy-based polynomial-time heuristic algorithm to reduce the computational complexity. The simulation results show that our enumerative algorithm not only effectively reduces the broadcasting delay but also ensures the fairness among SUs in resource sharing. We also show that the performance gap between our enumerative algorithm and our heuristic algorithm is small.

A slow-paced attack, such as slow worm or bot, can remain undetectable indefinitely by slowing down the pace of its movement. Detecting slow attacks based on traditional anomaly detection techniques may yield high false alarm rates. Since attacks are usually controlled by pre-programmed computer codes, their behaviors have regularity. In this paper, we track outbound connections of hosts by using a time series. Although the correlation among slow attacks' connections is temporally weak; the regularity of these connections remains preserved in the time series. Accordingly, we focus on time series spectrum analysis, and propose a detection method to identify peculiar spectral patterns which can represent the occurrence of a recurring and persistent activity in the time domain. We use both synthesized traffic and real-world traffic to evaluate our method. The results show that our method is efficient and effective in detecting slow-paced persistent activities even in a noisy environment with legitimate traffic.

To enhance the utilization of base stations (BSs) and face the challenge of the upcoming mobile data tsunami, energy saving and traffic offloading are two important issues to address in green cellular networks. In this paper, we design an optimal green policy, called Genie, to strike a balance between energy saving and traffic offloading in the heterogeneous cellular network, so that the system can either activate hotspot cells for traffic offloading or to deactivate the hotspot cells for energy saving. We prove that the optimal green policy is monotone hysteretic, and our system can thus be realized by simple switch-up and switch-down thresholds, while avoiding the ping-pong effect suffered by existing works. We show via simulations that our optimal green policy can significantly reduce total energy cost and perform energy saving and traffic offloading intelligently under all traffic conditions.

In this paper, we study how to improve the usage of wireless spectrum for secondary users (SUs) in cognitive radio networks (CRNs) by cooperative transmissions. There are two types of cooperation in CRNs, namely, vertical cooperation, which takes place between primary users (PUs) and SUs, and horizontal cooperation, which occurs among SUs. For vertical cooperation, PUs can lease their frequency bandwidth for a fraction of time to SUs in exchange for better transmission quality through cooperative transmission from the SUs. For horizontal cooperation, some SUs will help other SUs to increase their transmission throughput through cooperative transmissions without interfering with their neighboring PUs. In fact, both vertical cooperation and horizontal cooperation can improve the system throughput. Hence, if both types of cooperation can be supported in a CRN, the system throughput can be improved effectively. However, SUs cannot perform both types of cooperation simultaneously. Therefore, determining the kind of cooperation for an SU is an important issue on the system throughput improvement. We solve this problem via a greedy distributed solution in which the decision made by each SU depends only on the local information. We show by simulations that our proposed method outperforms traditional vertical cooperation schemes or horizontal cooperation schemes in terms of better resource utilization and higher system throughput.

Recent research shows that in wireless wide area networks (WWANs), users who subscribe to multicast traffic from WWAN can exchange network-coded packets with one another via their secondary radio interfaces such as Wi-Fi in order to efficiently recover lost packets from the WWAN. Different from existing works which assume users are cooperative, in this work, we model the users as selfish players in the network-coding based peer-to-peer packet repairing game. To stimulate the users' cooperation, we introduce a payment-based incentive mechanism in the packet repairing game. The utility function of a user/player is also formulated to reflect both the number of useful packets and the available resource. Through analysis of the packet repairing game, we show that the optimal strategy for a user can be derived only with its local information. The impact of the pricing rules and the convergence conditions of the packet repairing game is also analyzed. We show theoretically as well as by simulation that under proper conditions, the packet repairing game can converge to the best case where each user can acquire all of its missing packets. Via computer simulations, we also show that with the proposed selection criteria, the packet repairing game is both effective and efficient: not only can the utilities of the players be greatly improved, but also the convergence time of the game and the utility gain of the players are comparable to those of the ideal case where every user is always willing to forward packets to others.

Cooperative multicasting is a promising technique to combat severe fading and path loss for wireless video delivery. In typical wireless video multicasting, nodes under the worst channel condition determine the video delivery quality, thereby limiting the system performance and affecting the Quality of Experience (QoE) of the users. Existing cooperative multicast schemes may not fully exploit the characteristics of scalable video over wireless networks. To tackle this problem, we propose two new cooperative multicast schemes called OppCM and CodedCM for scalable video delivery. OppCM performs opportunistic listening and conditional demodulating to enhance the system performance; CodedCM delivers layer-encoded video with multi-resolution modulation through cooperative multicasting. We analyze the system performance in terms of average outage probability for four cooperative multicast schemes, including direct multicast (for baseline comparison), pure cooperative multicasting, and the two proposed schemes OppCM and CodedCM. We also evaluate their performance via simulations. The results show that the proposed OppCM and CodedCM outperform the other two schemes, demonstrating that they are indeed promising solutions to wireless scalable video multicasting.

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. The most effective way for energy conservation of BSs is to switch on/off BS adaptively. However, such operations may induce the problem of creating coverage holes in the network. In this paper, we attempt to minimize the total power consumption of the network by switching on and off BSs adaptively while maintaining the network coverage. Specifically, we derive the optimal cell size and determine the optimal number of active BSs for power consumption minimization with network coverage preservation. We also propose a near-optimal polynomial-time heuristic algorithm for energy-efficient BS activation with network coverage preservation. The simulation results show that our algorithm can approach the performance upper bound with only approximately 3dB performance loss in overall network power consumption for a cellular network with various deployment strategies.

In this paper, we propose a new protocol which integrates intra-flow network coding based opportunistic routing with inter-flow network coding, called CAOR, for lossy wireless networks. In a lossy environment, each linear combination of packets in a batch may not be able to arrive at each forwarder intact. The challenge of this problem is to determine which packets to be XORed for transmissions at each forwarder based on the partially received packets within all combinations (i.e., coded packets). The design goal of our solution is to reduce the number of transmissions and to increase coding opportunity through finding a proper set of packets which can increase the innovative probability for downstream nodes while ensuring the decodability for upstream nodes. The simulation results show that CAOR achieves an average throughput 20% to 30% higher than existing work. Moreover, CAOR also reduces on the average 20% of the number of transmissions for sending a batch of packets.

Network coding has been broadly applied to improve the efficiency of wireless multicast. In this paper, we consider the multicast process in modern relay-assisted wireless communication systems such as the IEEE 802.16j and the LTE-advanced networks, where the relay stations can cooperatively forward network-coded packets to the subscriber stations using different transmission rates. We show that under such multi-rate environments, previous solutions which seek to minimize the packet forwarding counts may lead to longer multicast delay. To solve this problem, in this work, we aim at finding a minimal delay transmission schedule of the relay stations under multi-rate considerations. We first show that this problem is NP-hard. Then we use a Markov decision process to model the relay station re-transmission process. Via this model, we derive the formulations for optimal re-transmission strategies as well as optimal re-transmission delays. Moreover, based on the recursive structure of the re-transmission delays derived from the model, we propose a dynamic programming algorithm which can solve optimal re-transmission strategies for the system. For complexity considerations, we also propose two light-weight on-line re-transmission heuristics. Simulation results show that the Markov decision process can accurately characterize the relay re-transmission process in network-coding-enabled wireless relay networks, and that minimal multicast delay can be achieved by dynamic programming-based relay re-transmissions. Moreover, simulation results suggest that the two heuristics may be suited to different scenarios, and both can achieve near-optimal performances efficiently.

The majority of previous studies have focused on the analyses of an entire graph (network) or the giant connected component in a graph. Here we study the disconnected components (non-giant connected components) in real social networks, and reporting some interesting discoveries on how these disconnected components evolve over time. We study six diverse, real networks (citation networks, online social networks, academic collaboration networks, and others), and make the following major contributions: (a) we make empirical observations of the longevity distribution of disconnected components, and find that the curve of the distribution demonstrates a decaying trend; (b) we find that the distributions of final size of disconnected components that merge with one another or get absorbed by the giant connected component both follow power laws; (c) we find that the majority of mergings are between disconnected components and the giant connected component. The mergings that happen among disconnected components are small in scale (involve only a few components). The longevity distributions of the disconnected components in those mergings are similar, where the shortest-lived disconnected components are the most in number; and (d) we propose an empirical generative model that can produce the networks with our observed patterns.

In this paper, we study the cooperative strategies of relay stations in wireless relay networks. In such networks, relay stations distributed across the network and centrally controlled by the base station can be exploited to form cooperative antenna arrays. We show that via relay station cooperation, the achievable downstream rate of mobile stations can be enhanced. Thus, we determine which relay stations should cooperate with one another and which mobile stations should receive data from the cooperative relays such that the performance of the entire network can be optimized. The utility maximization relay station cooperation problem is formulated with the goal of maximizing system capacity and balancing user traffic demand. We analyze the complexity of this problem and show that it can be decomposed into two NP-hard sub-problems. To tackle this problem, we propose two algorithms with different degrees of efficiency and complexity. The simulation results show that relay station cooperation can not only enhance the throughput of the network, but also improve fairness among users. More importantly, we find that proper relay station placement can further enhance the throughput gain.

In this paper, we study the Roadside Unit (RSU) placement problem in vehicular networks. We focus on the highway-like scenario in which there may be multiple lanes with exits or intersections along the road. In our model, each vehicle can access RSUs in two ways: 1) direct delivery, which occurs when the vehicle is in the transmission range of the RSUs, and 2) multi-hop relaying, which takes place when the vehicle is out of RSU transmission range. We account for both access patterns in our placement strategy and formulate this placement problem via an integer linear programming model such that the aggregate throughput in the network can be maximized. We also take into account the impact of wireless interference, vehicle population distribution, and vehicle speeds in the formulation. The performance of the proposed placement strategy is evaluated via ns-2 simulations together with VanetMobisim to generate vehicle mobility patterns. The results show that our strategy leads to the best performance as compared with the uniformly distributed placement and the hot spot placement. More importantly, our solution needs the least number of RSUs to achieve the maximal aggregate throughput in the network, indicating that our scheme is indeed a cost effective yet highly efficient placement strategy for vehicular networks.

In this paper, we propose a congestion-aware network-coding-based opportunistic routing mechanism called CONCOR for wireless ad hoc networks. An existing mechanism called MORE shows good throughput in the single flow case; however, it suffers significant performance degradation in scenarios with multiple traffic flows due to the redundant transmission problem and the unbalanced load problem. To tackle these two problems, our proposed CONCOR contains two key components, namely, null space feedback and rank differential, which allows feedback of the received random network coding information with minimal overhead among neighboring nodes and enables a backpressure policy to balance the load along the path. The simulation results show that CONCOR outperforms MORE in all cases, even in the single flow scenario. Specifically, compared with MORE, CONCOR has better throughput due to fewer redundant transmissions and more equitable treatment of multiple competing flows.

Cooperative multicast is a promising technique to combat severe fading and path loss for wireless video delivery. In typical video multicast, nodes under the worst channel condition determine the video delivery quality, thereby limiting system performance. To tackle this problem, we propose a new cooperative multicast scheme called CodedCM, which delivers layer-encoded video with multi-resolution modulation through cooperative multicast. We analyze the system performance in terms of average outage probability for three multicast schemes, including conventional multicast (for baseline comparison), pure cooperative multicast, and the proposed CodedCM, and compare their performance via simulations. The simulation results show that the proposed CodedCM scheme outperforms the other two schemes, demonstrating that it is indeed an excellent solution to wireless scalable video multicasting.

IPTV multicasting is an important service for residential users. In this paper, we explore how to allocate resources to different layer-encoded IPTV programs using multicast services in relay-assisted centralized wireless access networks. We consider the scenario of two-hop relay stations, and the MAC protocol is OFDMA-based, as in IEEE 802.16m or Long Term Evolution-Advanced (LTE-A). We formulate this problem via an Integer Linear Programming model and propose a heuristic algorithm called Maximum Utility resource allocation for Relayed Multicast Services (MU-RMS) to solve this problem. The simulation results show that our algorithm performs efficiently. We also show that our performance is affected by how the forwarding strategy is performed.

In this paper, we study the cooperative network coding problem in relay-assisted wireless access networks. In such a network, each mobile station can choose different transmission schemes: directly transmitting to the base station, pure relaying by a relay station, or via cooperative network coding. We analyze the impact of the different schemes on the network throughput. We find the performance of pure relaying and cooperative network coding is affected by the selection of the relay station, making the relay selection problem inseparable from the cooperative network coding problem. We formulate the joint relay selection and resource allocation problem, and find this problem equivalent to the multiple 0/1 knapsack problem. To tackle this problem, we propose an efficient network-coding-based resource allocation algorithm, and define a coding gain metric. The simulation results show our algorithm significantly outperform other schemes. More importantly, the performance improvement by cooperative network coding is significantly affected by the number of relay stations and the distribution of mobile stations.

In this paper, we study the cooperative strategies of relay stations in wireless relay networks. As pure relay stations distributed across the network and centrally controlled by the base station can be exploited to form cooperative antenna arrays, we determine which relay stations should cooperate with one another such that the performance of wireless relay networks can be optimized. Specifically, we define and formulate the throughput maximization problem for wireless relay networks under practical model of relay station cooperation. We design an iterative search algorithm to solve the relay station cooperation problem. The proposed algorithm is proved via simulations to yield near-optimal solutions with only linear-time complexity. The simulation results also show that cooperative transmissions of relay station can significantly improve system throughput and reduce the handover probability of mobile stations. More importantly, the placement of relay stations is crucial to the throughput gain obtained by relay station cooperation.

Several contention-based Medium Access Control (MAC) protocols have been proposed for the vehicles to gain the radio channels to distribute active safety messages timely, e.g., Safety Critical Application (SCA) information, for inter-vehicle communications in Vehicular Ad Hoc Networks (VANETs). In these MAC protocols, a contention period is introduced before the vehicle for channel access so that we may not have the timely and reliably message dissemination in VANETs. To reduce the contention period, this paper proposes a Region-based Clustering Mechanism (RCM) to be applied in these MAC protocols. We propose analytical models to investigate the performance of the RCM, which are validated by the simulation experiments.

In this paper, we study the relay station (RS) placement strategy in IEEE 802.16j WiMAX networks. Specifically, the impact of RS placement on IEEE 802.16j network performance is analyzed. A throughput maximization RS placement problem is mathematically formulated as a binary integer programming problem. We prove the NP-hardness of the formulated problem. To find the sub-optimal solution to the problem with huge input size, we propose an efficient near-optimal placement solution for IEEE 802.16j WiMAX networks. Simulations on the IEEE 802.16j network performance with our RS placement strategy are conducted. The throughput performance shows that with the deployment strategy we proposed, the IEEE 802.16j network capacity can be tremendously enhanced, especially when hotspots are present in the network.

In this paper, we study the utility-based resource allocation problem for layer-encoded IPTV multicast service over WiMAX networks. In this problem, each video stream is encoded into multiple layers. We regard each layer as a multicast subsession. Each layer of a video stream is assigned a utility value, and the number of layers for each program each user can receive is adjustable. The objective is to maximize the total utility (i.e., all users' satisfaction) and the system resource utilization, subject to users' channel conditions, the popularity of a video program, and the total available radio resource. We design a polynomial-time solution to this problem, and show that the difference in the performance of our proposed mechanism and the optimal solution is tightly bounded. Our mechanism supports both unicast and multicast, and both single layer and multi-layer environments. Most importantly, it can be integrated with the multicast mechanism defined in WiMAX standards, and can also be applied to any kind of wireless networks which support adaptive modulation and coding schemes. The performance of our scheme is evaluated by simulation. The simulation results show that this scheme can allocate resource flexibly according to the utility function of each program, the popularity of each program, and the amount of total resource available in the network.

In this paper, we introduce the concept of "route robustness" for path selection in multi-hop cognitive radio networks. We demonstrate that the aggregate throughput and the robustness of routes determined by the proposed route selection strategy are superior to existing rate-based selection strategies. The rationale behind our approach is to guarantee a basic level of robustness for a set of routes (referred to as skeletons in this paper). Then, we select some routes from this robust route set and determine the spectrum to be allocated on each link along these routes such that the system throughput is maximized. We also design a polynomial time algorithm for this problem, and evaluate our proposed mechanism via simulations. The results show that our proposed algorithm indeed achieves a near optimal solution of this problem for multi-hop overlay CR networks.

Multipath routing has been a promising solution to improving reliability and system throughput for wireless mesh networks. However, existing schemes designed for system throughput improvement suffer the route-coupling problem (i.e., the determined multiple paths originating from the same source node typically stay very close to the shortest path to the gateway) so that the improvement ratio may not be significant. In this paper, we propose a new routing algorithm called two concurrent path routing (2CPR) for wireless mesh networks with multiple gateways. In 2CPR, the routing paths are selected in such a way that the inter-path interference problem is minimized. Therefore, the route-coupling problem can be mitigated. Our proposed 2CPR scheme can be easily integrated with existing link metrics designed for tackling inter-flow interference, and each source node can make routing decision based on local information. The simulation results show that our scheme can outperform existing schemes, demonstrating that it is an excellent routing solution to wireless mesh networks with multiple gateways.

WiMAX is a promising solution for last-mile broadband Internet access. 802.16m is a new IEEE standard featuring multi-carrier technology to meet the requirement of IMT-Advanced for next generation mobile WiMAX. The design goal of IEEE 802.16m is to achieve 1Gbps over-the-air data rate with the help of multi-carrier operation. However, multi-carrier operation brings many challenges. In this paper, we study the call admission control problem together with multi-carrier assignment in IEEE 802.16m networks. We formulate an optimization problem and propose a two-stage carrier assignment algorithm to solve it. The simulation results show that the proposed algorithm achieves satisfactory aggregate throughput and revenue, while maintaining fairness among users.

Network coding is an efficient technique to increase network capacity. COPE is a promising forwarding mechanism which can utilize network coding in a practical way. Different routing algorithms utilizing COPE may have different gains from network coding. In this paper, we discuss the importance of coding-aware routing and the tradeoff between load-balanced and coding-aware routing. We then propose a new coding-aware routing scheme, called C2AR, which combines the advantages of load-balancing and network coding, and conduct ns-2 simulations to evaluate the performance of our proposed scheme. The simulation results show that our proposed scheme has significant improvement over coding-oblivious routing.

In this paper, we propose a channel-hopping MAC protocol called dynamic hopping MAC (DH-MAC) to improve the utilization of spectrum in cognitive radio networks. In DH-MAC, each secondary user (SU) switches its operation channel with a channel-hopping sequence which is adaptive to the activity of primary users (PUs). Our proposed DH-MAC mechanism enjoys the following merits. 1) It needs no dedicated common control channel for operation. 2) It supports multiple rendezvous for multiple simultaneous transmission pairs of SUs within the interfering range. 3) The rendezvous channel of two SUs will not be blocked by PUs for a long time. 4) It allows SUs a higher probability to operate on available channels as compared with existing schemes. 5) One cognitive radio transceiver is sufficient. The simulation results show that DH-MAC outperforms existing schemes in terms of higher aggregate throughput and more robustness to the disturbance of PUs.

Network coding may be used to improve resource efficiency and transmission reliability in the network. In this paper, we explore linear network coding for data forwarding in delay tolerant networks, which enable data transmission with intermittent connectivity. Specifically, we focus on vehicular ad hoc networks with scheduled routes such as bus systems. Traditional forwarding schemes may be inefficient in such networks because data losses at intermediate nodes may cause long delay in data delivery and thus low delivery ratio in data forwarding. Therefore, improving transmission reliability in delay tolerant networks is an important issue. In this paper, we propose a new coding-based forwarding protocol called vCF for vehicular ad hoc networks under the scenario of bus systems. In vCF, each message is fragmented into a set of blocks and encoded via linear network coding. Each node then schedules coded blocks to transmit when a contact opportunity occurs, and drops blocks when the buffer overflows. The simulation results show that vCF has a higher delivery rate and lower delivery delay as compared with existing schemes.

Live SNG (Satellite News Gathering) services, featuring real-time streaming of high-quality video, are typically enabled via expensive SNG vehicles and satellite transmissions by TV companies. In this paper, we design a cost-effective solution to delivering live news broadcasting (LNB) service over emerging broadband wireless access technologies such as WiMAX. However, LNB is a bandwidth hungry service and may consume most of the uplink wireless resource. Besides, the conditions of the shared wireless access between mobile nodes and base stations (BSs) may vary with time and location, which affect the quality of service (QoS) of LNB service. To guarantee the QoS level of LNB service, we implement a novel system called i-SNG which uses multi-link SCTP to glue together more bandwidth from heterogeneous wireless networks such as WiMAX, 3.5G cellular, and WiFi, and to ensure fault tolerance for LNB. We share our experience in constructing an experimental test-bed for our proposed i-SNG system for LNB service. We apply a new multi-link SCTP patch over FreeBSD 7.1 in this test-bed. The experimental results show that i-SNG not only provides a cost-effective solution to LNB service, but also preserves the perceptual quality of LNB video streaming under different network conditions.

Cognitive radio network (CRN) is designed to improve the low resource utilization problem caused by the fixed spectrum allocation policy used today. In CRNs, secondary users (SUs) sense spectrum holes and adaptively use the frequency band. In this paper, we tackle the Quality of Service (QoS) routing problem in wireless ad hoc CRNs. We consider such factors as available time, frequency bands, transmission range, error rate, primary user (PU) interruption rate and transmission range, and design a new QoS routing metric for wireless ad hoc CRNs. We then evaluate the performance via simulations in terms of the average and maximum end-to-end delays. The results show that our proposed metric outperforms existing solutions, including probability based, bit-rate based, and hop-count based methods.

In this paper, we introduce the concept of "route robustness" for path selection in multi-hop cognitive radio networks. We demonstrate that the aggregate throughput and robustness for routes determined by the introduced route robustness selection strategy are much better than those determined by other selection strategies as proposed in existing work. The rationale behind our approach is to guarantee a basic level of robustness for each route. We then jointly determine the routes and the spectrum allocation on each link along the routes such that the system throughput is maximized. The performance of the proposed mechanism is evaluated via simulations. The results show that our proposed mechanism can achieve better performance than other routing strategies.

In this paper, we tackle the multicast problem with the consideration of the interference between multicast trees in multi-radio multi-channel wireless mesh networks (MR-MC WMNs). We consider a dynamic traffic model, i.e., multicast session requests arrive dynamically without any prior knowledge of future requests. Each node in the network acts as a Transit Access Point (TAP), and has one or multiple radios tuned to non-overlapping channels. We prove that in MRMC WMNs, the minimum cost multicast tree (MCMT) problem, i.e., finding the multicast tree with minimum transmission cost, is NP-hard. We then formulate the problem by an Integer Linear Programming (ILP) model to solve it optimally, and propose a polynomial-time near-optimal algorithm, called Wireless Closest Terminal Branching (WCTB), for the MCMT problem. To alleviate the interference between multicast trees (sessions), we present a polynomial-time algorithm that computes the minimum interference minimum cost path in MR-MC WMNs, and integrate it into WCTB without altering the performance bound of WCTB on the tree cost. The experimental study shows that the tree cost produced by WCTB is very close to the optimal and that the proposed algorithm for interference alleviation is effective. To the best of our knowledge, this is the first paper that studies the MCMT problem in MR-MC WMNs.

In this paper, the network deployment problem for IEEE 802.16j networks is studied. We consider jointly deploying a number of base stations and relay stations to serve mobile stations distributed arbitrarily in a given geographic area such that the cost is within the budget and the system capacity is maximized. This problem is formulated as an integer linear programming (ILP) model. We analyze the complexity of the problem and design an efficient two-stage network deployment algorithm which tackles both user fairness and capacity enhancement. Computational experiments are conducted to show the effect of network deployments in different traffic distribution scenarios. We also show the impact of different deployment profiles on network capacity and fairness and discuss the cost-efficiency tradeoffs between base stations and relay stations. Our numerical results indicate that deploying relay stations can enhance system capacity and fairness under the condition that the number of base stations deployed is not too small. The degree of enhancement is higher when the traffic demand distribution is concentrated in hotspots.

Relative location estimation plays an important role of localization in wireless sensor networks (WSNs). In WSNs with planned deployment of anchor nodes, some prior information may be available. Existing work on relative location estimation rarely takes into account the lognormal fading effect of wireless channel and the prior probability of the link distance to each reachable anchor node. As a result, when applied to such environments, they may not work effectively. In this paper, we propose a new model called Probability-based Maximum Likelihood (PML) for relative location estimation. With some prior information, the estimation accuracy can be improved significantly. We also discuss the impact of over-estimation and under-estimation of the distance to each reachable anchor node on the accuracy of relative location estimation, and introduce the concept of the compensation factor to combat such effects. The simulation results show that the proposed PML outperforms existing solutions in terms of estimation accuracy for WSNs with planned deployment of anchor nodes.

In this paper, we study the issues of medium access control (MAC) mechanisms for multi-hop wireless networks with directional antennas. Specifically, we explore the location-dependent carrier sensing problem and the interference problem caused by the minor lobes of antennas. Existing solutions to directional antenna MAC problems rarely account for the impact of minor lobes and typically assume that the neighboring nodes' locations are known a priori. As a result, they are not applicable to practical systems or to mobile nodes. In this paper, we propose a Reservation-based Directional MAC (RDMAC) protocol for multi-hop wireless networks with directional antenna. This mechanism operates in sessions. Each session comprises two periods, namely, a reservation period and a transmission period. The reservation period is further composed of a set of two phases, namely, a probing phase and a beam-indication phase. The mechanism is designed to reduce the interference among neighboring nodes and to increase the network throughput. The performance of the proposed mechanism is evaluated analytically and via the ns2 simulator. The results show that our mechanism outperforms existing solutions in terms of higher throughput and lower delay.

In this paper, we study the determination of downlink (DL) and uplink (UL) channel split ratio for Time Division Duplex (TDD)-based IEEE 802.16 (WiMAX) wireless networks. In a TDD system, uplink and downlink transmissions share the same frequency at different time intervals. The TDD framing in WiMAX is adaptive in the sense that the downlink to uplink bandwidth ratio may vary with time. In this work, we focus on TCP based traffic and explore the impact of improper bandwidth allocation to DL and UL channels on the performance of TCP. We then propose an adaptive split ratio (ASR) scheme which adjusts the bandwidth ratio of DL to UL adaptively according to the current traffic profile, wireless interference, and transport layer parameters, so as to maximize the aggregate throughput of TCP based traffic. Our scheme can also cooperate with the base station (BS) scheduler to throttle the TCP source when acknowledgements (ACKs) are transmitted infrequently. The performance of the proposed ASR scheme is validated via ns-2simulations. The results show that our scheme outperforms static allocation (such as the default value specified in the WiMAX standard and other possible settings in existing access networks) in terms of higher aggregate throughput and better adaptivity to network dynamics.

This paper studies the impact of node density on the end-to-end throughput and delay in multi-hop wireless networks. In existing work, each packet is most assumed to be relayed through one cell at each hop and the hop progress is approximated by the square root of a cell area, which does not correspond to the actual hop progress in the real network. In this paper, we calculate the hop progress by taking into account the effect of node density (i.e., the number of nodes within the transmission range of each node), and obtain the required hop count for a multi-hop path. Based on the result, we further discuss the scaling relations between node density and throughput and delay in multi-hop wireless networks. The effects of power control on the scaling relations are also examined. The results show that the impact of node density on the throughput and delay scaling is significant. Specifically, with a larger node density, the required hop count is reduced, resulting in exponential growth of the throughput. However, larger node density incurs more contentions among neighboring nodes. Consequently, it causes linear degradation in throughput. With our model, this trade-off is readily observed.

This paper addresses service differentiation with interference consideration for traffic with different priorities in multi-hop wireless networks. Specifically, we propose a crosslayer framework which supports different service levels in terms of queuing delays for concurrent sessions of different priorities. The system architecture is composed of two major components: a priority-based flow scheduler and an interference-aware bandwidth allocation unit. The priority-based flow scheduler differentiates the queueing delay for data packets being relayed to the next hop according to their priorities. As a result, the sessions of higher priority are guaranteed to have lower queueing delay at each intermediate node on the path to the receiver while the starvation of the lower priority session can be avoided. To utilize wireless resources optimally, we formulate the bandwidth allocation problem with interference consideration as a convex optimization problem. The problem can be solved by a subgradient algorithm in a distributed fashion. We then develop a distributed protocol for our proposed algorithm. The simulation results show that the proposed algorithm can achieve different levels of bandwidth allocation efficiently with a limited number of iterations. In addition, our algorithm scales well when the number of sessions and the size of the session increase. Together with the priority-based flow scheduler, the end-to-end throughput and delay can be effectively differentiated based on different levels of bandwidth allocation.

QoS routing in multi-channel wireless mesh networks (WMNs) with contention-based MAC protocols is a very challenging problem. In this paper, we propose an on-demand bandwidth-constrained routing protocol for multi-radio multi-rate multi-channel WMNs with the IEEE 802.11 DCF MAC protocol. The routing protocol is based on a distributed threshold-triggered bandwidth estimation scheme, implemented at each node for estimating the free-to-use bandwidth on each associated channel. According to the free-to-use bandwidth at each node, the call admission control, which is integrated into the routing protocol, predicts the residual bandwidth of a path with the consideration of inter-flow and intra-flow interference. To select the most efficient path among all feasible ones, we propose a routing metric which strikes a balance between the cost and the bandwidth of the path. The simulation results show that our routing protocol can successfully discover paths that meet the end-to-end bandwidth requirements of flows, protect existing flows from QoS violations, exploit the capacity gain due to multiple channels, and incurs low message overhead.

This paper focuses on vehicle to roadside unit (V2R) communications in vehicle networks based on IEEE 802.11 DCF MAC protocol. The main challenge is due to the short residence time of the moving vehicle within the coverage of each stationary RSU. As a result, the contentions among vehicles during this short period of time dramatically degrade the system performance. In this paper, we propose a new mechanism called Proxy-based Vehicles to RSU access (PVR) for V2R communications. This protocol is designed to exploit collaborative and opportunistic forwarding between any two spaced apart RSUs and to emulate back-to-back transmissions within the coverage of RSU. We then conduct ns-2 simulations to evaluate the performance of PVR. The results show that it achieves excellent performance and outperforms all existing solutions for V2R communications.

Employing multiple channels can effectively improve the network capacity in wireless mesh networks (WMNs). In multi-radio multi-channel WMNs, the channel assignment problem is to assign each radio a channel such that the network capacity is maximized. Since whether two nodes can communicate with each other depends on the channels they use, different channel assignments may lead to different network topologies. Most existing channel assignment algorithms are based on specific network topologies, such as trees, A-connected graphs, etc. This pre-determined network topology is then used as the input of the channel assignment algorithm. In this paper, we study the importance of the input network topology of channel assignment algorithms on the network performance, and propose an algorithm for constructing efficient input network topologies for existing channel assignment algorithms to increase the capacity gain due to multiple channels. The simulation results show that the proposed algorithm improves the network capacity dramatically without the need to modify existing channel assignment algorithms.

Currently, IP multicast and Explicit Multi-Unicast (Xcast) are two approaches for multicast communications. IP multicast is designed for large groups but is not scalable in terms of the group number because every router in a multicast tree needs to store the forwarding state for each group. In contrast, Xcast is designed for small groups and is not scalable in terms of the group size because each packet’s header can include only the addresses of a few receivers. Therefore, the two approaches are designed for different scenarios and address different problems. However, the selection of IP multicast or Xcast is left to end users who will choose the corresponding API in the applications. In other words, the scalability of the network relies on the end users, and is not guaranteed by the protocol itself. In this paper, we address the above issues and propose a multicasting protocol that is scalable and adaptive in terms of both group number and group size. We avoid the disadvantages of IP multicast and Xcast by choosing only a few routers to store the forwarding states, and multicast packets are delivered via Xcast among these routers. The main advantage of our protocol is that the assignment of forwarding states is optimal and performed in a fully distributed manner. We show that IP multicast and Xcast are two extreme and special cases of our approach. We prove that the overhead of our protocol is limited. In addition, the assignment of forwarding states in our protocol is adaptive to the dynamic group membership and the change in network topology. Moreover, our protocol is simple and can be extended from existing multicast routing protocols.

In this paper, we model the location-dependent throughput and delay in wireless mesh networks. We analyze packet arrival rates and packet departure rates for the forwarding queues at relaying nodes and then derive the throughput and packet delay experienced by nodes at different hop count distances to the gateway. Based on this model, we further analyze how network design strategies affect the throughput and delay of each node. We then conduct simulations to validate our analytical model and evaluate the performance of different network design strategies. This paper not only provides a framework for studying the location-dependent throughput and delay in wireless mesh networks but also gives insights into the network design strategy for wireless mesh networks.

In this paper, we study adaptive bandwidth allocation for TCP-based best effort traffic in IEEE 802.16j networks with relay stations. We consider the effect of bandwidth asymmetry on the performance of TCP and take into account the current traffic profile and network channel condition for each subscriber station. We then propose an adaptive scheme which enhances the system aggregate throughput and ensures fairness among TCP flows. Our scheme also cooperates with the scheduler to throttle the TCP source when acknowledgements are infrequent. The performance of our scheme is validated via ns-2 simulations. The results show that our scheme outperforms static allocation in terms of higher aggregate throughput.

In this paper, we focus on call admission control (CAC) in IEEE 802.11 multi-radio multi-rate multi-channel (MR2-MC) wireless mesh networks (WMNs). CAC is the key component of QoS routing protocols. The goal of CAC is to protect existing flows from QoS violations and fully utilize available radio re

In this paper, we propose a scalable and adaptive multicast forwarding mechanism based on explicit multicast (Xcast). This mechanism optimizes the allocation of forwarding states in routers and can be used to improve the scalability of traditional IP multicast and source-specific multicast. Compared with previous work, our mechanism needs fewer routers in a multicast tree to store forwarding states and therefore leads to a more balanced distribution of forwarding states among routers. We focus on two problems and formulate each of them as an optimization problem. The first problem, referred to as minstate, minimizes the total number of routers that store forwarding states in a multicast tree. The second problem, referred to as balancestate, minimizes the maximum number of forwarding states stored in a router for all multicast groups, which is proved to be an NP-hard problem. We design a distributed algorithm that obtains the optimal solution to the first problem and propose an approximation algorithm for the second problem. We also prove that the approach adopted by most existing works to allocate forwarding states in the branching routers of a multicast tree is a special case of our mechanism. The simulation results show that the forwarding state allocation provided by previous work is concentrated on the backbone routers in the Internet, which may cause the scalability problem. In contrast, our mechanism can balance forwarding states stored among routers and reduce the number of routers that store the forwarding states for a multicast tree.

In this paper, we study utility-based resource allocation for soft QoS traffic in infrastructure-based wireless networks. Soft QoS traffic here refers to the traffic which demands certain amount of bandwidth for normal operation but allows some flexibility when the given bandwidth is close to the preferred value. The resource requirement of soft QoS traffic can be described with sigmoid utility function. Our objective is to maximize the total utility of all soft QoS flows without going through a wireless bidding process. We develop essential theorems as the design guidelines for this problem, and then propose a sub-optimal, polynomial time solution based on the developed theorems. We prove that the difference in the performance of our mechanism and the optimal solution is bounded. The performance of the proposed solution is evaluated via simulations. The results show that our solution can adapt to any types of soft QoS flows. Specifically, it acts like a hard QoS system and allocates resource in a fairness-oriented manner when the utility functions of flows are unit-step functions; on the other hand, when the utility functions are concave, it behaves like a best effort system and allocates resource in a throughput-oriented way.

In this paper, we study the fairness problem in multi-hop wireless backhaul networks in the presence of selfish transit access points (TAPs). We design an incentive-based mechanism which encourages TAPs to forward data for other TAPs, and thus eliminates the location-dependent unfairness problem in the backhaul network. We prove the correctness and truthfulness of the proposed mechanism, and evaluate its performance via ns-2 simulations. The results show that the proposed mechanism achieves fairness even when there are idle TAPs in the network.

This paper addresses the flow allocation problem in multi-hop wireless networks. We define and formulate a new interference model, referred to as the Node-based Interference Model, to better capture the behavior of medium access control protocols and the physical layer interference issues. Based on this model, we formulate the problem as a cross-layer network utility maximization problem that considers the coordination of the transport, MAC and physical layers, and avoid the maximum clique or independent set enumeration approach as adopted in most of the existing work. The objective of the problem is to maximize the aggregate network throughput while maintaining the fairness among flows. We then propose a gradient-based flow allocation algorithm by using the duality approach, and analyze the rate of convergence to the optimum for the proposed algorithm. The simulation results show that the proposed algorithm can rapidly converge to the optimum, and can also rapidly adapt to the changes in network topology and routing paths in different flow scenarios.

This paper revisits the topology control problem for multi-hop wireless ad hoc networks. It accounts for the maximum number of links interfering with each node in the network, which is referred to as the interference load in the paper. Finding a topology with minimum interference load is an NP-complete problem. In this paper, we develop the essential theorems and lemmas for constructing low interference load topologies for wireless ad hoc networks. Based on the theoretical results, we provide the design guidelines for topology construction with low interference load, and propose a polynomial-time solution accordingly. The performance of the proposed mechanism is evaluated via simulations. The results show that the proposed mechanism outperforms existing work in terms of lower interference load even using the maximum transmission power in the generated topology.

In this paper, we study adaptive bandwidth allocation for uplink and downlink channels in time division duplex (TDD)-based IEEE 802.16 (WiMAX) wireless networks. In a TDD system, uplink and downlink transmissions share the same frequency at different time intervals. The TDD framing can be adaptive in the sense that the downlink to uplink bandwidth ratio can vary with time. For WiMAX channels, we consider the impact of improper bandwidth ratio on the performance of TCP and propose an adaptive bandwidth allocation scheme (ABAS) which adjusts the bandwidth ratio according to the current traffic profile. Our scheme also cooperates with the scheduler to throttle the TCP source when acknowledgements are infrequent. The performance of our mechanism is validated via ns-2 simulations. The results show that our scheme outperforms static allocation in terms of higher aggregate throughput.

In this paper, we propose a utility-based resource allocation scheme for layer-encoded IPTV multicast streaming service over IEEE 802.16 WiMAX networks. Unlike existing utility-based schemes, this mechanism is designed for wireless networks which support adaptive modulation and coding. Each video stream (or program) is encoded into different layers. Then, our mechanism adjusts the number of each user's received layers dynamically according to its channel condition and the available network bandwidth, so as to maximize total utility. We prove that this problem is NP-hard, and show that our scheme is bounded in performance to the optimal solution and can run in polynomial time. The simulation results show that this scheme can allocate resource flexibly according to the utility function of each program, the popularity of each program (i.e., the number of users receiving each program), and the amount of total available resource in the network. The result also shows that the fairness of the system can be guaranteed.

The interaction between MAC protocols and interference in wireless multi-hop networks introduces extra constraints on achievable flow rates. In this paper, we formulate an optimization problem which considers jointly interference at the physical layer, MAC protocols, and end-to-end flow rate allocation. We then propose an interference-aware flow allocation algorithm to achieve optimal and fair flow rates in multi-hop wireless networks. In our study, the interference is characterized by the interference factor R, i.e., the ratio of the interference range to the transmission range, while the MAC protocols are characterized by contention resolution mechanisms. Based on our formulation, the impact of interference and MAC protocols on end-to-end flow performance is investigated. Our simulation results show that the end-to-end throughput is significantly degraded when R exceeds two. ACK message exchanges are also more robust to interference variation as compared to contention resolution in MAC protocols.

In this paper, we study the behavior of voice over IP (VoIP) traffic over IEEE 802.11 wireless networks. Specifically, we propose a QoS provisioning mechanism for VoIP traffic, and suggest a practical solution to configuring the 802.lie enhanced distributed control access (EDCA) parameter sets for different types of traffic. With our mechanism, the EDCA parameter sets can be easily configured via software interface in off-the-shelf WiFi phone products and it is not required to modify the operations of access points or 802.11 MAC layer protocols. The performance of our mechanism is evaluated via ns-2 simulations and via laboratory experiments over Quanta's 02 dual-mode handsets. The results show our mechanism can provide effective and efficient QoS provisioning for VoIP traffic over IEEE 802.11 WLANs.

Voice over IP (VoIP) has experienced tremendous growth in recent years due to its low cost and flexible service enhancement. However, it is vulnerable to security attack. The most popular solution to providing secure VoIP service is based on the advanced encryption standard (AES). The practice for AES-based solution is to adopt a common secret key negotiated during a VoIP call setup phase. This single common key solution is, however, vulnerable to many kinds of attacks. In this paper, we propose a distributed multi-key solution which dynamically changes the encryption key based on the Diffie-Hellman algorithm to provide more secure protection for an end-to-end VoIP call. The proposed mechanism does not need to renegotiate new keys during a call and is robust to network dynamics and packet losses. Most importantly, it incurs small delay overhead comparable to the solutions with encryption protection. The performance of the proposed mechanism is evaluated via laboratory experiment, and validated by some security analysis.

In this paper, we provide service differentiation with interference consideration for traffic of different priorities in multi-hop wireless networks. Specifically, we propose a framework to support different service levels in terms of queuing delays at each intermediate path node for concurrent sessions of different priorities. We formulate an interference-aware bandwidth allocation problem which jointly considers the interference and resource allocation problems. We then develop a sub-gradient algorithm to solve this problem in a distributed manner. The simulation results show that our proposed algorithm achieves different levels of bandwidth allocation efficiently with a limited number of iterations. In addition, our algorithm scales well when the number of sessions and the size of the session increase.

This paper studies all-optical multicast routing in wavelength-routed optical networks with sparse light splitting. In a sparse splitting network, only a small percentage of nodes is capable of light splitting, i.e., multicast capable, and most of the nodes are multicast incapable. The typical approach to this problem is combining an existing Steiner tree heuristics with some rerouting procedure to refine the trees. Therefore, the cost in terms of the total number of wavelengths used for all tree links (referred to as wavelength channel cost) is very high. In this paper, we propose a new mechanism that constructs light-trees for sparse splitting optical networks without additional rerouting. We design two efficient schemes to build a light-tree for any given multicast session. We then extend our mechanism to support dynamic group membership. The simulation results show that our mechanism can build light-trees with the least wavelength channel cost and with the smallest number of wavelengths used per link.

In this paper, we propose a new multicast delivery mechanism for bandwidth-demanding applications in IP networks. Our mechanism, referred to as multiple-destination overlay multicast (MOM), combines the advantages of IP multicast and overlay multicast. We formulate the MOM routing problem as an optimization problem. We then design an algorithm based on Lagrangian relaxation on our formulation and propose a distributed protocol based on the algorithm. For network operators, MOM consumes less network bandwidth than both IP multicast and overlay multicast. For users, MOM uses less interface bandwidth than overlay multicast.

In this paper, we analyze the saturation performance of IEEE 802.11e enhanced distributed channel access (EDCA), which provides contention-based differentiated channel access for frames of different priorities in wireless LANs. With EDCA, quality of service (QoS) support is provided with up to four access categories (ACs) in each station. Each AC behaves as an independent backoff entity. The priority among ACs is then determined by AC-specific parameters, called the EDCA parameter set. The behavior of the backoff entity of each AC is modeled as a two-state Markov chain, which is extended from Bianchi's model to capture the features of EDCA. The differences of our model from existing work for 802.11e EDCA include: (i) virtual collisions among different ACs in an EDCA station are modeled, thus more accurately capturing the behavior of EDCA; (ii) the influence of using different arbitrary inter-frame spaces (AIFS) for different ACs on saturation performance are considered; (iii) delay and delay jitter are derived, in addition to saturation throughput. The analytical model is validated via ns-2 simulations. The results show that our analytical model can accurately describe the behavior of IEEE 802.11e EDCA

In this paper, we study utility-based maximization for resource allocation in the downlink direction of centralized wireless networks. We consider two types of traffic, i.e., best effort and hard QoS, and develop some essential theorems for optimal wireless resource allocation. We then propose three allocation schemes. The performance of the proposed schemes is evaluated via simulations. The results show that optimal wireless resource allocation is dependent on traffic types, total available resource, and channel quality, rather than solely dependent on the channel quality or traffic types as assumed in most existing work.

The Ethernet Passive Optical Network (EPON) is a promising solution of metropolitan optical networks. In this paper, we model the performance of TCP over EPON in terms of fairness. We identify the fairness problem suffered by EPON using the existing polling scheme. We then derive two tight bounds of fairness indices for downloading users, and propose two polling mechanisms to improve unfair resource sharing among multiple TCP flows. The performance of the proposed mechanisms is evaluated by simulations via ns2. The results show that our mechanisms can improve fairness among the downloading flows without degrading the aggregate throughput and the fairness of the uploading flows.

The forwarding progress in one hop plays an essential role for many tradeoff problems in ad hoc networks. In all previous analytical work, nodes are assumed to be Poisson distributed. However, this assumption has its limitation and cannot be applied to many scenarios. In this paper, a more general model is proposed and the forwarding progress in one hop with arbitrary nodal distribution and different forwarding strategies is computed. The accuracy of the model is verified by simulations.

In this paper, we study interference-efficient topology control for wireless ad hoc networks. A node is interfered by a communication link if it receives a transmission not intended for it. In this study, we account for the number of communication links contributing interference to each node in the generated topology, defined as interference load of a node. Two problems are investigated in this paper. The first problem is to find a connected topology with lower interference load. The second problem is to construct a topology under the spanner constraint. We propose two heuristic algorithms, namely, Low Interference- load Neighborhood Forest (LILNF) and Low Interference-Load Spanner Topology (LILST) for each problem, respectively. In comparison to existing Low Interference Forest Establisher (LIFE) and Low Interference Spanner Establisher (LISE) algorithms, LILNF and LILST can significantly reduce the interference load in the resulting topology while the total interference load and transmission range are still maintained.

In this paper, we provide an analytical model to predict the deviation of distance estimation in the hop-count based localization scheme. Specifically, we develop a probability distribution function of per-hop progress for wireless networks with arbitrary node density, and predict the estimation error of hop-count based localization. The correctness of our model is validated via simulations. Based on this model, we can obtain the localization errors of hop-count based schemes without conducting tedious simulations when such network parameters as node density and transmission range are given. In addition, the impact of different network parameters on the estimation error of hop-count based localization can also be examined.

In this paper, we study utility-based resource allocation for soft QoS traffic in wireless networks. We describe the design guidelines for this problem and propose a sub-optimal, polynomial time solution for such allocation. The proposed mechanism accounts for the traffic type, the total available resource, and the user channel quality, rather than solely depending on channel quality or traffic type as assumed in most existing work. The performance of the proposed mechanism is evaluated via simulations. The results show that this mechanism adapts to different traffic types. Specifically, it acts like a hard QoS system and allocates resource in a fairness-oriented manner when the utility function is very steep; on the other hand, when the utility function is relatively flat, this mechanism behaves like a best effort system and allocates resource in a throughput-oriented way.

Employing multiple channels in wireless multi-hop networks is regarded as an effective approach to increasing network capacity. However, existing routing protocols may not be able to properly utilize the advantages of multiple channels in such networks. In this paper, we focus on IEEE 802.11-based wireless mesh networks with stationary nodes, such as wireless backhaul networks and community wireless networks. We propose a new path metric called Bottleneck Link Capacity (BLC) which accounts for the link quality, the interference among links, and the traffic load on the links. Then, we develop a routing protocol called Capacity-Aware Routing (CAR) which makes use of BLC as the routing metric. Finally, we evaluate the performance of BLC via simulations. The results show that our path metric outperforms others in terms of system throughput and end-to-end delay.

In this paper, we study interference-efficient topology control for wireless ad hoc networks. A node is interfered by a communication link if it receives a transmission not intended for it. In this study, we account for the number of communication links contributing interference to each node in the generated topology, defined as the interference load of a node. Our objective is to find a connected topology with lower interference load. We prove that finding a topology with minimum interference load is NP-hard. We propose a heuristic algorithm, namely, Low Interference-load Topology (LILT) to find a feasible solution in polynomial time. In comparison to the existing Low Interference Forest Establisher (LIFE), we find that LILT can significantly reduce the interference load in the resulting topology while the total interference load and transmission range are still maintained.

In this paper, we model the link duration of the random waypoint model for mobile ad hoc networks. The link duration is the time interval in which two nodes stay within transmission range of each other. We find that link duration is determined by the relative speed and active distance between two nodes, which are in turn determined by the angles of the two nodes' velocities and the incident angle of one node to the other node's transmission range. We derive the probability distribution function of link duration for the random waypoint model and verify the analytical result via simulations. The result show that the analytical and simulation curves match very well

The Ethernet Passive Optical Network (EPON) is a promising solution for metropolitan optical networks. In EPON, the downstream channel is a point-to-multipoint broadcast network and the upstream channel is a multipoint-to-point network based on a polling mechanism. In this paper, we model the performance of TCP over EPON with respect to the aggregate throughput and fairness. We calculate the maximum aggregate throughput of TCP flows on each of the upstream and downstream channels, and identify the fairness problem with existing polling schemes for EPON. We then derive two tight bounds of the fairness index for downloading users, and propose two polling mechanisms to improve unfair resource sharing among multiple TCP flows. The performance of the proposed mechanisms is evaluated via ns-2 simulations. The results show that our mechanisms can improve fairness among downloading TCP flows without degrading the aggregate throughput and fairness of uploading TCP flows.

This paper studies the impact of the data-over-cable service interface specification (DOCSIS) media-access control (MAC) protocol on the performance of the transmission control protocol (TCP) in hybrid fiber coax (HFC) broadband access networks. We find that the asymmetry ratio expressed in existing work cannot adequately explain the behavior of TCP in DOCSIS-based networks. To better capture the effect of DOCSIS on TCP, we express the asymmetry ratio (denoted by /spl eta/) in another way (denoted by k), considering the time-division multiple-access-like MAC layer operation of DOCSIS. When /spl eta/>1, TCP behaves as in a symmetric network, and when /spl eta//spl les/1, the system acts as in an asymmetric network, and the performance of TCP degrades. We find that the number of simultaneous TCP transfers significantly affects the asymmetry ratio. When the number of active transfers is below two times the maximum number of pending requests in a transmission period, the value of /spl eta/ is larger than one, regardless of the value of k. However, when the number of active transfers becomes very large, the effect of DOCSIS on TCP becomes negligible, and the asymmetry ratio is determined by the bandwidth ratio of the channels times the length ratio of data and acknowledgement packets. Based on /spl eta/, we develop the round-trip delay of sending a data packet for both one-way and two-way transfers, and discuss the buffer requirement at the head end. The accuracy of the analytical model is validated by ns-2 simulations. The analytical result can provide useful guidelines in the design of slot allocation or scheduling mechanisms for any DOCSIS-based broadband access networks, including the emerging IEEE 802.16 WiMAX networks.

In this paper, we analyze node mobility for reliable packet delivery in mobile IP networks. In mobile IP, packets destined to roaming nodes are intercepted by their home agents and delivered via tunneling to their care of addresses (CoA). A mobile node may roam across multiple subnets. At each boundary crossing, a handoff is initiated such that the CoA is updated and a new tunnel is established. We consider both basic mobile IP handoff and smooth handoff. We find that reliable packet delivery in mobile IP networks can be modeled as a renewal process, because the retransmission over a new tunnel after each boundary crossing is independent of the previous history. We then derive the probability distribution of boundary crossings for each successful packet, based on which the packet reliable delivery time can be obtained. Our analytical model is derived based on a general distribution of residence time in a subnet and a general distribution of successful retransmission attempts in each subnet. The results can be readily applied to any distributions for both items. We also provide numerical examples to calculate the probability distribution of boundary crossings, and conduct simulations to validate our analytical results

In this paper, we study fair queuing in the MAC layer of IEEE 802.11 with the distributed coordination function (DCF). In particular, we propose an IFS-based distributed fair queuing (IDFQ) mechanism to provide differentiated service in conformance with the 802.11 standard. IDFQ is designed to emulate self-clocked fair queuing (SCFQ) in a distributed manner. There is no backoff in IDFQ. Thus, it provides better stability and improved aggregate throughput, as compared to existing work. Simulation results show that IDFQ supports differentiated service for different flows in proportion to their weights. More importantly, it outperforms existing solutions in terms of fairness and stability, rendering IDFQ an excellent candidate to provide weighted fairness for IEEE 802.11 WLANs.

In this paper, we study utility-based optimal resource allocation in wireless networks. Utility here refers to a function which describes the degree of user satisfaction with a certain amount of allocated resource. We introduce utility function maximization in a centralized wireless environment and consider two types of traffic, i.e., best effort and QoS, the characteristics of which can be described by two existing utility functions. An allocation for the mixture of QoS and best effort traffics is proposed and its performance is evaluated via simulations. From this study, we show that optimal wireless resource allocation depends on the traffic types, the total available resource, and the channel quality, rather than solely dependent on the channel quality or traffic types as assumed in most existing work.

In this paper, we provide a differentiated service model in IEEE 802.11e wireless local area networks (WLANs). In our service model, a new mechanism called differentiated service - EDCA (DS-EDCA) is proposed to provide both strict priority and proportional fair service for IEEE 802.11 WLANs. The EDCA parameter set of lower priority traffic is carefully designed, and the backoff intervals are determined according to the distributed scheduling discipline DFS. Furthermore, a hierarchical link sharing model is proposed for IEEE 802.11e WEANs, which allocates different amount of link resource for AP and mobile stations. The performance of DS-EDCA and EDCA is compared via ns-2 simulations. The results show that DS-EDCA outperforms the original EDCA in terms of the support for both strict priority and weighted fair service. Most importantly, DS-EDCA can be easily implemented, and is compatible to the IEEE 802.11 standard.

Lists the Autonomic Networks Program Committee ((in alphabetical order).

In this paper, we study reliable multicast at the MAC layer for IEEE 802.11 Wireless LANs. In IEEE 802.11, multicast transmissions are unreliable in the sense that multicast frames are transmitted from the access point (AP) without an ACK being returned from each receiver as in unicast transmissions. As a result, transmitted multicast frames may be lost due to collisions or errors. There are two types of reliable multicast at the MAC layer proposed for 802.11 in the literature: one is ACK-based and the other is a leader-based mechanism. For the ACK-based mechanism, the AP monitors the frame reception progress of all receivers and retransmits frames whenever no ACK is received from any receiver; for leader-based mechanism, the AP retransmits frames only when no ACK frame is received from the selected leader. We analyze their performance in terms of frame holding time at the AP, and conduct simulation to validate our analytical model. We also propose a new channel acquisition and multicast notification mechanism called RTS/CTS/SEQ to improve the performance of existing work for reliable multicast in IEEE 802.11 WLANs.

In this paper, we model the behavior of packet forwarding via a multihop path in mobile ad hoc networks. In our analysis, we consider a densely populated network and assume a flooding protocol for packet forwarding. We find that the behavior of packet forwarding in such an environment can be regarded as dropping a stone into a lake, and then counting the number of ripples moving out from the source (i.e., the center) to the destination. What makes the problem complicated is the node mobility during the packet forwarding. As a result, we cannot solely count the number of ripples in between as the number of hops in the path on which the packet traverses. We then derive the probability distribution function of hop counts for packet forwarding, accounting for node movements. Based on the analytical model, we then evaluate several different types of flooding mechanisms commonly adopted for target searching in ad hoc networks. Compared with existing work, which assumes a snapshot of the network and all nodes are static in the analysis, our analytical framework provides more insights for the study of efficient flooding in mobile ad hoc networks.

In this paper, we study the proportional fairness problem in IEEE 802.11e wireless local area networks (WLANs). With 802.11e EDCA, only priority-based service is supported. Such priority-based service, while allowing differentiated service for flows of different priorities, cannot ensure service amount in proportion to their demands. This calls for weighted fair service to be supported by EDCA. In this paper, we propose a mechanism called weighted fair-EDCA (WF-EDCA) to provide proportional fairness for IEEE 802.11 WLANs. With WF-EDCA, weighted fair service among different access categories (ACs) is provided, and strict priority service can also be implemented. We then conduct simulations based on ns-2 to compare the performance of WF-EDCA and EDCA. The results show that WF-EDCA outperforms EDCA in terms of providing proportional fairness and strict priority service for IEEE 802.11 WLANs while retaining comparable total throughput.

In this paper, we study the provision of per-class QoS for IEEE 802.11e enhanced distributed channel access (EDCA) WLANs. We propose two mechanisms, called BIWF-SP and IDFQ-SP, based on backoff interval (BI) and inter-frame space (IFS), respectively. In our mechanisms, both strict priority and proportional fair service are supported. We describe the operations of the proposed mechanisms in details, and compare their performance with the original EDCA mechanism via simulations. The results show that both BIWF-SP and IDFQ-SP outperform the original EDCA in terms of the support for both strict priority and weighted fair service. Compared to IDFQ-SP, BIWF-SP is easier to be implemented in real systems; compared to BIWF-SP, IDFQ-SP has better aggregate throughput and is more stable. More importantly, both mechanisms conform to the IEEE 802.11e EDCA standard, rendering both good candidates to provide per-class QoS service for IEEE 802.11 WLANs.

In this letter, a new transport layer mechanism is proposed to improve the performance of transport control protocol (TCP) in mobile networks. The proposed mechanism is comprised of two parts: a loss classifier (LC) and a congestion window extrapolator (CWE). Based on LC, the cause of packet loss during roaming is determined. If the loss is considered to be caused by congestion in the wireline, the congestion window is halved; otherwise, the packet is considered to be lost in the last hop, the wireless portion, and the sender adjusts the size of the congestion window based on CWE. We conduct simulations to evaluate the performance of the proposed mechanism. The results show that our mechanism significantly improves TCP performance as compared with existing solutions for mobile networks.

In this paper, we propose a new approach to establishing application layer conference trees for multimedia multipoint conferences on the Internet using the Megaco/H.248 protocol, a Voice over IP (VoIP) media gateway control protocol. In existing VoIP protocols (and also legacy telephone networks), a multipoint conference takes place through an MCU, and forms a star topology centered at the MCU. This paper suggests to establishing shared, cost effective conference trees for VoIP conferences. Each tree is rooted at the conference initiator, who initiates the conference, and spans over all the conference members. Tree branches grow or are trimmed dynamically and adaptively, in a way to avoid the growth of a skewed tree. We develop a simplified analytical model and conduct simulations to evaluate the performance of the proposed approach. The results show that our approach enjoys the advantage of lower join latency and better bandwidth efficiency compared to the traditional MCU approach, and is cost effective compared to a near optimal Steiner tree.

We study fair scheduling in ad hoc networks, accounting for channel errors. Since wireless channels are susceptible to failures, to ensure fairness, it may be necessary to compensate those flows on error-prone channels. Existing compensation mechanisms need the support of base stations and only work for one-hop wireless channels. Therefore, they are not suitable for multihop wireless networks. Existing fair scheduling protocols for ad hoc networks can be classified into timestamp-based and credit-based approaches. None of them takes channel errors into account. We investigate the compensation issue of fair scheduling and propose a mechanism called Timestamp-Based Compensation Protocol (TBCP) for mobile ad hoc networks. We evaluate the performance of TBCP by simulation and analyze its long-term throughput. The results show that our analytical result provides accurate performance estimation for TBCP.

In this paper, we propose a bandwidth-efficient multicast routing mechanism using rectilinear Steiner trees for IP-based LEO satellite networks. Different from the previous approach which minimizes the end-to-end delay, our mechanism minimizes the total bandwidth, i.e, the number of hops, used by a multicast tree since the wireless bandwidth in satellite networks is a limited and scarce resource. We propose a new integer linear programming formulation for network planning and a distributed algorithm for protocol design. Our simulation results show that the trees created by our approach use less bandwidth than shortest-path trees. Moreover, the difference of the optimal solutions and the solutions obtained by our distributed algorithm is within 5% on average.

This work studies TCP-friendly congestion control to support non-adaptable multimedia flows over the Internet. Existing TCP-friendly congestion control mechanisms assume that flow senders can adjust their sending speeds at a fine granularity to match the requests from the congestion control protocols. In reality, the companion CODEC at the sender guides the source sending rates. To date, the supportable rates of any CODEC are "discrete", not "continuous". Therefore, from the prospective of the transport layer protocols, these data flows are non-adaptable. This renders existing congestion control mechanisms inadequate for real environments. We propose a TCP-friendly congestion control protocol called adaptive multirate transport protocol (AMRTP) to support non-adaptable multimedia flows. We also conduct simulations to evaluate the performance of AMRTP. The results show that AMRTP is TCP friendly, and also has superior performance as compared to existing work.

We propose a new forwarding mechanism for IP multicast based on explicit multicast (Xcast). Compared with traditional IP multicast, our mechanism achieves a more flexible allocation of multicast forwarding states among routers. Previous works only focus on reducing the number of routers which store the forwarding states, ignoring the distribution of forwarding states among routers. The number of forwarding states stored in each router may be unbalanced. We formulate the assignment of the routers with forwarding states as two optimization problems. The first one is to minimize the number of routers with the forwarding states in each multicast tree. The second one is to minimize the maximum number of forwarding states stored in a router. We design several algorithms for both problems. We prove that our algorithms can find the optimal solution to the first problem. By simulation, we also show that our algorithms perform well in the second problem. Moreover, we prove that assigning all branching routers as the only routers with the forwarding states is a special case in our mechanism. Due to higher flexibility, we show that our mechanism use fewer forwarding states, and the distribution of forwarding states is more balanced.

The hybrid fiber coax (HFC) network is a widely-used technology of broadband access networks to the home. The data-over-cable service interface specifications (DOCSIS) designed by the Multimedia Cable Network System Partners (MCNS) is the de facto standard in the cable industry. This paper studies the contention effect of DOCSIS in HFC networks. In particular, we focus on the performance of TCP. We develop a Markov model to estimate the probability of a CM transmitting a request in a randomly selected mini-slot. Based on the derived request transmission probability, the mean access delay for TCP transfers is calculated. We also conduct simulations via network simulator version 2 (ns-2) to validate the analysis.

We analyze the handoff behavior for reliable packet delivery in mobile IP networks. In mobile IP, packets destined to roaming hosts are intercepted by their home agents and delivered via tunneling to its care of address (CoA). A mobile node may roam across multiple subnets during receiving data reliably. Upon each boundary crossing, a handoff is initiated in which the CoA is updated and a new tunnel is established. We find that reliable packet delivery in mobile IP networks can be modeled as a renewal process. We derive the probability distribution of boundary crossings for each successfully transmitted packet. We also provide numerical examples to demonstrate how to use our model to calculate the probability distribution of boundary crossings, given the distributions of residence time and local retransmission attempts.

This paper proposes an adaptive slot allocation mechanism to improve the performance of Transmission Control Protocol (TCP) in DOCSIS-based hybrid fiber coaxial (HFC) networks. The proposed mechanism is comprised of two parts: fast request transmission (FRT) and long packet deferment (LPD). FRT is designed to handle one-way TCP transfers, while LDP targets two-way transfers. Here "one-way transfers" means all active cable modems perform downloading; while "two-way transfers" indicates some perform downloading and some perform uploading. We analyze the proposed mechanism and conduct simulations using network simulator ns-2 to evaluate the performance of our mechanism. The results show that the proposed mechanism has better performance in terms of aggregate downstream throughput, access delay, and required buffer size, as compared to the original control mechanism of DOCSIS.

We study fair scheduling with quality of service (QoS) support for wireless ad hoc networks. Two types of flows are considered: best effort and guaranteed flows. The goal is to satisfy the minimum bandwidth requirements of guaranteed flows and to provide a fair share of residual bandwidth to all flows. We compare timestamp-based and credit-based mechanisms, and evaluate the feasibility of all existing fair scheduling schemes of these two types of mechanisms for multimedia wireless multihop networks. We suggest a flow weight calculation scheme for existing timestamp-based mechanisms to support both best effort and guaranteed flows, and propose a credit-based mechanism called credit-based slot allocation protocol (CSAP) for fair scheduling with QoS support. For comparison purposes, several metrics are defined to evaluate the performances of these two kinds of mechanisms. The simulation results show that CSAP outperforms the other approaches in terms of meeting the minimum requirements of guaranteed flows, fairly sharing the residual bandwidth among all flows, and improving overall system throughput.

This paper studies self-configuring random early detection (RED) gateways for quality of service (QoS) networks. Existing work only considers the of RED gateways for networks with best effort flows. This paper focuses on QoS networks in which both assured and best effort flows co-exist. RED with in/out (RIO) is an extension of RED for QoS networks, which maintains two parameter sets (i.e., in-profile and out-of-profile) for different classes of traffic. We find that fixed and low out-of-profile thresholds limit best-effort throughput when assured flows are frequently absent or the amount of assured flows is small. We then propose an adaptive scheme called adaptive-RIO to adjust the parameters according to network dynamics. From the simulation, adaptive-RIO performs better than RIO in terms of higher best-effort throughput and total throughput while ensuring the committed rate of assured flows.

In this paper, we formulate an optimization problem for the design of light-tree based logical topology in wavelength division multiplexing (WDM) networks. The problem is comprised of two parts: (1) multicast routing and wavelength assignment of light-trees, and (2) the design of light-tree based logical topology for multicast streams. In the first part, we use mixed integer linear programming (MILP) to solve the optimal routing and wavelength assignment problem of light-trees with an end-to-end delay bound, and obtain the optimal placement of power splitters and wavelength converters. The numerical results show that networks with just a few power splitters and wavelength converters can efficiently carry multicast data. In the second part, we extend the above formulation to design the logical topology based on light-trees for multicast streams. In our approach, a light-tree can carry data of multiple multicast streams, and data of a multicast stream may traverse multiple light-trees to reach a receiver. The numerical results show that our approach use network resources more efficiently, as compared to the approach with a separate light-tree for a multicast stream and to the approach of transporting multicast streams over lightpath based logical networks.

This paper studies all-optical multicast routing in wavelength-routed optical networks with sparse light splitting. In a sparse splitting network, only a small percentage of nodes are capable of light splitting, i.e., multicast capable. The typical solutions of existing multicast routing algorithms for sparse splitting networks combine an existing Steiner tree heuristic with some rerouting procedures to refine the trees. The resulting tree cost in terms of the total number of wavelengths used on all tree links is then very expensive. In this paper, we propose a new mechanism that constructs all-optical multicast trees for sparse splitting networks without an additional rerouting procedure in the tree construction. Two efficient approaches are suggested and evaluated by simulations. The results show that our mechanism builds light-trees with the least wavelength channel cost and with the smallest number of wavelengths used per link.

In this paper, we study the code assignment problem in high speed downlink packet access (HSDPA) networks. In HSDPA, the spreading factor of codes is fixed at 16, and the maximum number of bits reserved for code assignment transmitted on the control channel is limited to 64 bits. This high control overhead may lead to inefficiencies in HSDPA systems when the number of mobile nodes in a cell becomes large. Existing code assignment mechanisms allow codes with different spreading factors to be allocated, rendering them inadequate for HSDPA systems. In this paper, a new segment-based code assignment mechanism is proposed. In our mechanism, 16 codes are grouped into several segments. Since the number of codes in each segment is reduced, the corresponding signaling overhead is also reduced. The load of each channel is then balanced in each segment so as to maximize the system performance.

This paper studies resource management for multimedia mobile ad hoc networks (MANET). In particular, we focus on providing fair scheduling with quality-of-service (QoS) support for MANET. We consider two types of flows: guaranteed and best effort flows. The goal is to satisfy the QoS requirements of guaranteed flows and to provide global fairness for best effort flows. In this paper, a credit-based fair scheduling mechanism called credit-based slot allocation protocol (CSAP) is proposed. In CSAP, nodes are logically grouped into clusters, each with a scheduler. Each scheduler assigns time slots to nodes in its cluster based on the first tier algorithm. The node scheduled to send at the next time slot then in turn assigns the time slot to a relayed flow determined by the second-tier algorithm. Each multihop flow is treated as multiple single-hop flow segments. These segments are then correlated such that a downstream segment will not be allocated a slot unless the upstream segments have all been allocated. We evaluate the performance of CSAP by simulations. The results show that CSAP meets the QoS requirements of guaranteed flows, provides global fairness for best effort flows, and improves overall system throughput.

This paper proposes a preemptive multiclass wavelength reservation protocol to provide differentiated service for Optical Burst Switched (OBS) networks. Unlike existing approaches, which may degrade to classless schemes or which may suffer from low wavelength utilization, our mechanism is robust, efficient, and supports an incremental deployment of QoS support. We maintain a usage profile for each class at the router, and implement a preemptive wavelength reservation mechanism to ensure QoS. We derive an analytical model and conduct simulations to evaluate the performance. The result shows that our approach performs the best in terms of lower blocking probability and higher resource utilization, making it an excellent QoS mechanism for OBS networks.

This paper proposes a new protocol, called family ACK tree (FAT), to support reliable multicast service in ad hoc networks. FAT employs a tree-based recovery mechanism that localizes ACKs and retransmissions to avoid feedback implosion. To cope with node movements, FAT constructs an ACK tree in which each node maintains reachability information to three generations of family in the tree. When a tree is fragmented due to a departed node, the fragments will be glued back to the tree using the underlying multicast routing protocol. FAT then adopts an adaptive mechanism to recover missed packets that have been multicast to the group during fragmentation. We have conducted simulations to evaluate the performance of FAT and to compare FAT with the existing solution. The results show that FAT achieves better performance in providing reliable service in ad hoc networks, in terms of reliability, scalability, and delivery efficiency.

This paper studies the effect of the DOCSIS MAC layer on the performance of two-way TCP transfers in hybrid fiber coax (HFC) networks. We propose a new adaptive scheduling scheme called long packet deferment (LPD) at the headend to improve TCP performance in DOCSIS-based HFC networks. LPD reduces the frequency of transmission of long packets and, if such long packets are transmitted, they are scheduled towards the end of each transmission period. Thus, it allows the system to behave as in a symmetric network earlier, reduces the round trip delay of sending data packets, and improves the aggregate downstream throughput. We have conducted simulations using network simulator ns-2 to compare the simple first-come-first-served scheduling of DOCSIS and IEEE 802.14 like mechanism with LPD. The results show that LPD has better performance in terms of higher aggregate downstream throughput and shorter access delay.

This paper studies the effect of DOCSIS MAC layer on TCP performance in hybrid fiber coax (HFC) networks. We discuss how the DOCSIS MAC mechanism affects bandwidth asymmetry, analyze TCP behavior over DOCSIS MAC layer, and propose a new mechanism called fast request transmission (FRT) to improve TCP performance in HFC networks. We have also conducted simulations using network simulator ns-2 to compare the original control mechanism of DOCSIS with our mechanism. The results show that the proposed FRT mechanism has better performance in terms of aggregate downstream throughput, access delay, and buffer size.

The paper studies fair scheduling with quality of service (QoS) support in ad hoc networks. The goal is to satisfy the minimum bandwidth requirements of guaranteed flows and to provide a fair share of residual bandwidth among all flows. We propose a new cluster-based mechanism called two-tier slot allocation protocol (2TSAP), which improves system throughput and ensures per-cluster fairness while satisfying the requirements of guaranteed flows. With 2TSAP, the network is logically partitioned into clusters, each with a scheduler. The scheduler assigns time slots to mobiles in the affiliated clusters based on the first tier algorithm. The mobiles scheduled to send at the next time slot then in turn assign the time slot to flows determined by the second tier algorithm. We compare the performance of 2TSAP with Q EMLM-FQ, a virtual-time based fair scheduling mechanism with QoS support. The simulation results show that 2TSAP meets the minimum requirements of guaranteed flows, shares the residual bandwidth fairly among all flows, and improves overall system throughput.

This paper studies analytically the performance of reliable multicasting for host mobility in IP networks. In particular, we examine two schemes: global token rotating and local recovery with tunneling. Both schemes support reliable multicast for mobile hosts at the expense of extra buffer spaces in mobility agents. The token rotating mechanism uses a rotating token which reports global packet reception status for all the agents in the system to cope with host mobility. An agent cannot remove a packet out of the buffer unless all the mobile hosts in the global system have correctly received the packet. The local recovery with tunneling approach, on the other hand, relieves this global waiting constraint from each agent. It requires each mobile to be registered with one agent, but changeable to a different agent upon handoff. The packets not repairable by new agents are then tunneled from previous agents. Each agent cannot remove a packet unless all the registered hosts under its service have correctly received the packet. We model the buffer requirements of both protocols as an M/G//spl infin/ queuing system, from which we can further derive service latency and system throughput. We have also conducted simulations to verify the derived analytical model. The results show that the local recovery with tunneling mechanism has much smaller buffer size, higher throughput, and lower service latency, as compared to the token rotating mechanism.

This paper explores the issues of multicasting for mobile hosts using IETF mobile IP. Mobile IP suggests two approaches for mobile multicasting, namely, remote subscription (MIP-RS) and bi-directional tunneling (MIP-BT). MIP-RS uses a naive join-and-leave policy for mobile hosts and causes serious packet losses while roaming. MIP-BT is free from packet losses due to roaming; however, MIP-BT and its modification MoM suffer from inefficient routing and wastage of system resources. In this paper, we propose a new mobile multicasting protocol called mobile multicast with routing optimization (MMROP) that employs a modified join-and-leave mechanism, and routes the missing data sequence due to the "out-of-synch" problem to other agents in the adjacent networks via tunneling for roaming terminals. With MMROP, the mobility agent in mobile IP is extended to assist multicasting for mobiles. We also developed a cost analytical model to evaluate the performance of MMROP, in comparison to MIP-RS, MIP-BT, and MoM. The results demonstrate that MMROP has optimal routing efficiency, low delivery cost and high robustness, as compared to other approaches.

This paper proposes a new TCP variation called Formosa that significantly improves TCP end-to-end performance in terms of high connection throughput and high degree of fairness in resource sharing among multiple competing connections in asymmetric networks. Existing TCP implementations are based on the ACK-clock, which clocks out a single data packet on receipt of an ACK. Formosa TCP pushes the frequency of ACKs per round trip time (RTT) to the lowest limit, using an ACK to cumulatively acknowledge a window's worth of data packets. The congestion window is updated to reflect the available bandwidth estimated on the forward channel, without the aid of the intermediate routers. As a result, the number of ACK packets is significantly reduced, while the principle of "conservation of packets" is still maintained. Based on "an ACK per window," Formosa estimates forward delay and implements the additive increase and multiplicative decrease algorithm for better congestion avoidance. In addition, it employs the negative acknowledgement mechanism for better congestion control during loss recovery. As compared to existing implementations, TCP Formosa demonstrates the best performance in terms of high connection throughput, high degree of fairness among connections and robustness in the face of two-way transfers in asymmetric networks.

In this paper, we propose a new approach to estab- lishing application layer conference trees for multimedia multi- point conferences on the Internet using the Megaco/H.248 protocol, a Voice over IP (VoIP) media gateway control protocol. In existing VoIP protocols (and also legacy telephone networks), a multipoint conference takes place through an MCU, and forms a star topology centered at the MCU. This paper suggests to establishing shared, cost effective conference trees for VoIP conferences. Each tree is rooted at the conference initiator, who initiates the conference, and spans over all the conference members. Tree branches grow or are trimmed dynamically and adaptively, in a way to avoid the growth of a skewed tree. We develop a simplified analytical model and con- duct simulations to evaluate the performance of the proposed ap- proach. The results show that our approach enjoys the advantage of lower join latency and better bandwidth efficiency compared to the traditional MCU approach, and is cost effective compared to a near optimal Steiner tree.

IP multicasting is an efficient group communication mechanism. It avoids transmitting packets from a sender to each of the receivers separately. With the current IP multicast mechanism, once having joined a group, a host will receive all data destined to the group from any source, irrespective of whether it wishes to receive them. This paper studies source filtering in IP multicasting. Source filtering is the ability for an individual host to specify the reception of packets sent to a multicast group only from a list of source addresses or to explicitly identify a list of the sources whose data the host does not want to receive. We investigate the issue of source filtering in the context of multicast routing protocols, and propose a solution to support source filtering in IP multicast routing. We have also conducted simulations to evaluate the performance of the proposed mechanism. The results show that, compared to multicasting without the capability of source filtering, our mechanism allows better bandwidth utilization and scalability, thus achieving a truly efficient use of resources for IP multicasting.

This paper studies the integration of multimedia multicasting, with the consideration of multicast with end-to-end QoS guarantees by resource reservation, dynamic join and departure of participants, user heterogeneity, scalability, robustness, and loop-free control. A protocol called MQ, Multicast with QoS, is proposed to support multimedia group communications with QoS guarantees for heterogeneous recipients. With MQ, while resource reservation is de-coupled from QoS multicast routing, they are integrated in a way to avoid the problem of "sender-oriented" path determination, a problem that occurs when RSVP is used in conjunction with QoS routing for heterogeneous reservations. Being a truly receiver-oriented and integrated scheme for multimedia multicasting, MQ supports such integration in a robust, scalable and loop-free way. It also accommodates heterogeneous users with varied QoS, dynamically adjusts QoS trees to improve resource utilization, and guarantees end-to-end QoS requirements. We have conducted simulations to evaluate the performance of the proposed mechanism. MQ demonstrates its advantages over the conventional loosely coupled integration of TP multicasting, resource reservation and QoS routing, in terms of better accommodation of heterogeneous users, higher scalability, lower blocking probability for users to join groups with service guarantees, and more efficient resource utilization to enhance system performance.

This paper studies the issue of reliable multicast with host mobility using mobile IP. Mobile IP suggests two approaches for mobile multicasting, namely, remote subscription (MIP-RS) and bi-directional tunneled multicast (MIP-BT). However, both approaches suffer from problems when used as the basic mechanism to provide reliable multicast service for mobiles. MIP-RS enjoys efficient delivery, but suffers from serious packet losses during roaming due to using the naive join-and-leave policy. MIP-BT is free from packet losses due to roaming; however, it suffers from the triangular routing problem, resulting in inefficient routing and wastage of system resources. We propose a new protocol called reliable mobile multicast protocol (RMMP) to provide reliable multicast services for mobile IP networks. Our approach imposes no restriction on the particular mechanisms for reliable multicast in the wired part of the network, and is easily integrated with any existing reliable multicast protocols used for stationary hosts. Being derived from MIP-RS and with the assistance of mobility agents, RMMP routes the missing datagrams due to the out-of-synch problem in adjacent subnets via tunneling to ensure optimal delivery efficiency and to ensure no packet losses from roaming. We also conducted a simulation study to evaluate the performance of RMMP. The results demonstrate that RMMP has high delivery efficiency, low buffer requirement, and high robustness, as compared to other approaches.

This paper proposes a new protocol, the reliable mobile multicast protocol (RMMP), for mobile networks using mobile IP. Unlike existing mobile multicast solutions that employ bi-directional tunneling for roaming terminals to receive multicast datagrams and which is inefficient in packet delivery, wastes system resources and results in long service latency, RMMP ensures reliable multicast service for mobiles by using remote subscription. Remote subscription, however, suffers from the out-of-synch problem when data are received from the same multicast group but from different subnets while roaming from one subnet to another, thereby causing packet losses for roaming terminals. With RMMP, the mobility agent (foreign or home agent) in mobile IP, in addition to providing mobility management, is extended to serve as the reliable multicast agent for mobiles, and routes the difference in data sequence to other agents in the adjacent subnets via tunneling. As a result, RMMP provides reliable mobile multicast services while enjoying lower delivery cost, less service latency and better routing efficiency.

Internet telephony realizes the transmission of two-way, real-time, synchronous traffic over IP-based networks. Mobile Internet telephony introduces mobility to Internet telephony, allowing one to stay in communications while roaming. In this paper, a novel protocol called mobile Internet telephony protocol (MITP) is proposed. MITP is an application layer protocol based on the client/server model for mobile Internet telephony services. Signaling for mobility management, including registration, call establishment, and roaming, is accomplished through the exchange of defined request and response messages. Much of the message syntax is identical to HTTP/1.1 and SIP/2.0, thereby enabling code reuse and ease of integration of Web and SIP servers.

Internet multicast is an important networked service for many existing and emerging applications. The dominant mechanism for group management of IP multicasting is the Internet group management protocol (IGMP). IGMP is based on a query/reply model and refreshes group membership periodically. IGMP has been evolving through three versions. IGMP v1 develops the basis of the query/reply group management model and suppression mechanism, IGMP v2 reduces leave latency that IGMP v1 suffers from, and IGMP v3 adds the capability of source filtering but removes the suppression mechanism of IGMP v1/v2. In this paper, a new group management protocol called receiver-initiated group membership protocol (RGMP) is proposed. Both source filtering and membership report suppression are supported. An RGMP host actively refreshes group membership in the neighboring multicast routers. No querier, and hence no query messages and timers, is required for periodically probing the presence of known groups. An individual host maintains a refresh timer per group. The refresh timer is reset once the suppression rule holds for a received report message, where the report may be a join, leave, state change, or a periodical refresh message. The receiver-initiated, self-synchronized refresh timer makes the RGMP suppression mechanism superior to that of IGMP v1/v2, which applies only for periodical refresh report message. As a result, the protocol overhead compared to IGMP v3 is significantly reduced, over a wide variety of service scenarios. In addition to reduced protocol overhead, RGMP is robust, scalable and adaptive to serve as a group management protocol.

Internet telephony realizes the transmission of two-way and real-time traffic over IP-based networks. The dominant standard for Internet telephony is ITU-T Rec. H.323. With the current version of H.323, Internet telephony allows interoperability with circuit-switched telephone, but IP host mobility is not supported. In this paper, mobile extensions to H.323 that enable mobile Internet telephony service are proposed. The proposed approach combines the characteristics of both cellular phone system and mobile IP mechanism with Internet telephony, and therefore realizes the transmission of real-time voice traffic for both stationary and mobile hosts over IP-based networks. The most striking feature of the proposed approach is that the complicated mobility management functions are handled by the procedures for dynamically joining and departing from a conference, functions already defined in H.323. Therefore, our approach allows mobility support without the need for additional new entities, and with minimal modifications to existing H.323 standard. Such mobility extensions can serve as an add-on feature for the existing Internet telephony systems compliant to the H.323 standard.

A true video-on-demand (VOD) system provides the ultimate flexibility in video services by allowing users to select any video programs, at any time, and to perform any VCR-like user interactions. To allow true VOD, one approach is to have a dedicated video stream for each customer. This is expensive, especially when multiple identical video streams are sent to multiple customers accessing the same video. To be commercially viable, VOD service must be priced competitively with existing video rental services. Batching may be used to reduce this cost. It allows multiple users accessing the same video to share the same video stream. The batching approach, however, complicates the provision of user interactions. Existing batching schemes only allow near VOD services. This paper describes a new protocol, called split and merge (SAM), which offers true VOD services while allowing multiple users to share the same video stream. This sharing is transparent to the users and it appears as if each has a dedicated video stream. Our approach is to split an interactive user from the batch and to serve him with a dedicated video stream. We develop an innovative way to merge these individuals back to the batching streams when they resume normal play mode. The SAM protocol therefore significantly improves the system resource utilization and the number of simultaneous users, and more importantly, allows true VOD services.

A distributed multimedia system (DMS) is an integrated communication, computing, and information system that enables the processing, management, delivery, and presentation of synchronized multimedia information with quality-of-service guarantees. Multimedia information may include discrete media data, such as text, data, and images, and continuous media data, such as video and audio. Such a system enhances human communications by exploiting both visual and aural senses and provides the ultimate flexibility in work and entertainment, allowing one to collaborate with remote participants, view movies on demand, access on-line digital libraries from the desktop, and so forth. In this paper, we present a technical survey of a DMS. We give an overview of distributed multimedia systems, examine the fundamental concept of digital media, identify the applications, and survey the important enabling technologies.

An interactive video-on-demand (VoD) system allows users to access video services, such as movies, electronic encyclopedia, interactive games, and educational videos from video servers on a broadband network. This paper develops a performance evaluation tool for the system design. In particular, a user activity model is developed to describe the usage of system resources, i.e., network bandwidth and video server usage, by a user as it interacts with the service. In addition, we allow batching of user requests, and the effect of such batching is captured in a batching model. Our proposed queueing model integrates both the user activity and the batching model. This model can be used to determine the requirements of network bandwidth and video server and, hence, the trade-off in communication and storage costs for different system resource configurations.