As a core technique of machine learning, deep neural networks (DNNs) have been extensively used in today's mobile applications. However, users' mobile devices (MDs) have limited capabilities to execute computation-intensive DNN inference operations and meet the latency constraints. Offloading part of DNN computations to edge servers (ESs) in a mobile edge computing (MEC) network can mitigate these challenges. However, prior studies on offloading either overlook the varying computation demands and output data sizes for different layers of neural networks or only consider split DNN offloading based on a single user or a single ES. Driven by the question of how to partition multiple parallel DNN inferences and offload multiple partitioned DNN processes in a multi-user multi-ES network, in this paper, we design a distributed scheme that jointly optimizes user-ES association, DNN layer-level partitioning, computing and wireless communication resource allocation, and offloading. Specifically, MDs and ESs make decisions to maximize their own utilities based on a multi-leader multi-follower Stackelberg game by leveraging DNN layer characteristics and taking account of multi- server heterogeneous network environments, computation load, and available resources. The evaluation results show that the proposed joint optimization scheme can significantly improve the performance of DNN inferences, compared to the commonly used benchmarks.

Mobile edge computing (MEC) integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. This paper investigates the task assignment problem for cooperative MEC networks in which a set of geographically distributed heterogeneous edge servers not only cooperate with remote cloud data centers but also help each other to jointly process user tasks. We introduce a novel stochastic MEC cooperation framework to model the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment optimization problem is formulated by taking into consideration dynamic network states, uncertain node computing capabilities and task arrivals, as well as the heterogeneity of the involved entities. We then develop and compare three task assignment algorithms, based on different deep reinforcement learning (DRL) approaches, value-based, policy-based, and hybrid approaches. In addition, to reduce the search space and computation complexity of the algorithms, we propose decomposition and function approximation techniques by leveraging the structure of the underlying problem. The evaluation results show that the proposed DRL-based task assignment schemes outperform the existing algorithms, and the hybrid actor-critic scheme performs the best under dynamic MEC network environments.

Mobile edge computing (MEC) is an emerging platform that enables mobile devices to offload computation intensive tasks to the edge servers co-located with base stations (BSs) at the network edge for enhanced computation capabilities and low latency. This paper investigates the computation offloading problem in next-generation massive multiple-input multiple-output (M-MIMO) non-orthogonal multiple access (NOMA) MEC networks using a distributed alternating direction method of multipliers (ADMM) approach. Specifically, we develop a novel ADMM-based offloading algorithm to optimize latency and increase energy efficiency over next-generation mobile networks in a multiuser M-MIMO NOMA configuration. Simulation results demonstrate that the proposed offloading scheme significantly improves the system performance.

This paper investigates an air-ground integrated multi-access edge computing system, which is deployed by an infrastructure provider (InP). Under a business agreement with the InP, a third-party service provider provides computing services to the subscribed mobile users (MUs). MUs compete for the shared spectrum and computing resources over time to achieve their distinctive goals. From the perspective of an MU, we deliberately define the age of update to capture the staleness of information from refreshing computation outcomes. Given the system dynamics, we model the interactions among MUs as a stochastic game. In the Nash equilibrium without cooperation, each MU behaves in accordance with the local system states and conjectures. We can hence transform the stochastic game into a single-agent Markov decision process. As another major contribution, we develop an online deep reinforcement learning (RL) scheme that adopts two separate double deep Q-networks to approximate the Q-factor and the post-decision Q-factor, respectively. The deep RL scheme allows each MU to optimize the behaviours with unknown dynamic statistics. Numerical experiments show that our proposed scheme outperforms the baselines in terms of the average utility under various system conditions.

3D sensors such as LiDAR, stereo cameras, and radar have been used in many applications, for instance, virtual or augmented reality, real-time immersive communications, and autonomous driving systems. The output of 3D sensors is often represented in the form of point clouds. However, the massive amount of point cloud data generated from 3D sensors poses big challenges in data storage and transmission. Therefore, effective compression schemes are needed for reducing the bandwidth of wireless networks or storage space of 3D point cloud data. Several point cloud compression (PCC) algorithms have been proposed using signal processing or neural network techniques. In this study, we investigate four state-of-the-art PCC methods using two different datasets with various configurations. The objective of this study is to provide a comprehensive understanding of various approaches in PCC. The results of this paper will be helpful in developing an adaptive 3D point cloud stream compression benchmark that is efficient and benefited from different PCC techniques.

In this paper, we investigate a cloud radio access network (Cloud-RAN) in which both fronthaul and radio access links use massive MIMO millimeter-wave (mmWave) transmissions. Such an all-mmWave Cloud-RAN architecture provides a flexible and cost-effective means for deployment of next-generation (5G and beyond) cellular networks to meet the demands of fast-growing mobile data traffic. Nevertheless, the design of transmit power allocation schemes for multiple massive MIMO beams on both the fronthaul and access links in a mmWave Cloud-RAN is challenging. In particular, the traffic and wireless channel states of multiple mobile terminals (MTs) change over time, while their statistics may not be known a priori. We formulate the joint fronthaul-access link massive MIMO beam power allocation problem as a Markov decision process (MDP) with an objective to optimize the long-term quality of service to all the MTs in a Cloud-RAN. A reinforcement learning algorithm is designed, which learns the optimal beam power allocation policy on the fly and adapts to the network dynamics. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is developed to reduce the search space during the learning process. The evaluation results validate the convergence of our proposed scheme and demonstrate its superior performance over the state-of-the-art baselines.

This paper investigates the task management for cooperative mobile edge computing (MEC), where a set of geographically distributed heterogeneous edge nodes not only cooperate with remote cloud data centers but also help each other to jointly process tasks and support real-time IoT applications at the edge of the network. Especially, we address the challenges in optimizing assignment of the tasks to the nodes under dynamic network environments when the task arrivals, node computing capabilities, and network states are nonstationary and unknown a priori. We propose a novel stochastic framework to model the interactions of the involved entities, including the edge-to-edge horizontal cooperation and the edge-to-cloud vertical cooperation. The task assignment problem is formulated and the algorithm is developed based on online reinforcement learning to optimize the performance for task processing while capturing various dynamics and heterogeneities of node computing capabilities and network conditions with no requirement for prior knowledge of them. Further, by leveraging the structure of the underlying problem, a post-decision state is introduced and a function decomposition technique is proposed, which are incorporated with reinforcement learning to reduce the search space and computation complexity. The evaluation results demonstrate that the proposed online learning-based scheme outperforms the state-of-the-art benchmark algorithms.

Identifying heavy flows, i.e., flows with large packet counts during a pre-defined time window, is vital for many network applications. The task of real-time heavy flow detection in data plane is challenging due to high switching speed (100 Gbps), a large number of concurrent flows (millions of concurrent flows), and small memory footprint requirement. In this paper, we dissect the key factors that affect the existing detection scheme’s accuracy, and propose AccuPipe, a new detection scheme with intelligent flow entry replacement strategies. The simulation results show that the new scheme is able to efficiently utilize all flow entries in the detection pipeline, and detects more than 850 heavy flows (out of top 1,000) using a small amount of memory (1,000 flow entries, roughly equivalently to 18KB memory) with reasonable reporting overhead. This represents a 76% improvement over HashPIPE scheme, which detects on average 484 heavy flows (out of top 1,000) in the same setting. In addition, we investigate the performance of different flow entry replacement strategies, and report their pros and cons.

With the cellular networks becoming increasingly agile, a major challenge lies in how to support diverse services for mobile users (MUs) over a common physical network infrastructure. Network slicing is a promising solution to tailor the network to match such service requests. This paper considers a system with radio access network (RAN)-only slicing, where the physical infrastructure is split into slices providing computation and communication functionalities. A limited number of channels are auctioned across scheduling slots to MUs of multiple service providers (SPs) (i.e., the tenants). Each SP behaves selfishly to maximize the expected long-term payoff from the competition with other SPs for the orchestration of channels, which provides its MUs with the opportunities to access the computation and communication slices. This problem is modelled as a stochastic game, in which the decision makings of a SP depend on the global network dynamics as well as the joint control policy of all SPs. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game with the local conjectures of channel auction among the SPs. We then linearly decompose the per-SP Markov decision process to simplify the decision makings at a SP and derive an online scheme based on deep reinforcement learning to approach the optimal abstract control policies. Numerical experiments show significant performance gains from our scheme.

Millimeter wave (mmWave) communications provide great potential for next-generation cellular networks to meet the demands of fast-growing mobile data traffic with plentiful spectrum available. However, in a mmWave cellular system, the shadowing and blockage effects lead to the intermittent connectivity, and the handovers are more frequent. This paper investigates an "all- mmWave" cloud radio access network (cloud-RAN), in which both the fronthaul and the radio access links operate at mmWave. To address the intermittent transmissions, we allow the mobile users (MUs) to establish multiple connections to the central unit over the remote radio heads (RRHs). Specifically, we propose a multipath transmission framework by leveraging the "all- mmWave" cloud-RAN architecture, which makes decisions of the RRH association and the packet transmission scheduling according to the time- varying network statistics, such that a MU experiences the minimum queueing delay and packet drops. The joint RRH association and transmission scheduling problem is formulated as a Markov decision process (MDP). Due to the problem size, a low-complexity online learning scheme is put forward, which requires no a priori statistic information of network dynamics. Simulations show that our proposed scheme outperforms the state-of- art baselines, in terms of average queue length and average packet dropping rate.

The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role‐based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

Wireless data traffic, especially video traffic, continues to increase at a rapid rate. Innovative network architectures and protocols are needed to improve the efficiency of data delivery and the quality of experience (QoE) of mobile users. Mobile edge computing (MEC) is a new paradigm that integrates computing capabilities at the edge of the wireless network. This paper presents a computation‐capable and programmable wireless access network architecture to enable more efficient and robust video content delivery based on the MEC concept. It incorporates in‐network data processing and communications under a unified software‐defined networking platform. To address the multiple resource management challenges that arise in exploiting such integration, we propose a framework to optimize the QoE for multiple video streams, subject to wireless transmission capacity and in‐network computation constraints. We then propose two simplified algorithms for resource allocation. The evaluation results demonstrate the benefits of the proposed algorithms for the optimization of video content delivery.

Matrix inverse computation is one of the most fundamental mathematical problems in large-scale data analytics and computing. It is often too expensive to be solved in resource-constrained devices such as sensors. Outsourcing the computation task to a cloud server or a fog server is a potential approach as the server is able to perform large-scale scientific computations on behalf of resource-constrained users with special software. However, outsourcing brings in new security concerns and challenges such as data privacy violations and result invalidation. In this paper, we propose a secure and verifiable outsourcing scheme to compute the matrix inverse in a server. In our scheme, the client generates two secret key sets based on two chaotic systems, which are utilized to create two sparse matrices whose permuted versions are used for matrix encryption and decryption to protect input and output privacy. The server computes the inverse over the ciphertext matrix and returns the result to the client who can verify the validity of the inverse. We analyze the proposed scheme in terms of correctness, security, verifiability, and attack resistance, and compare its performance (computation, storage, and communication overheads) with those of the state-of-the-art. Our theoretical results and comparison study demonstrate that the proposed scheme provides a secure and efficient outsourcing mechanism for matrix inverse computation.

In this study, the authors investigate the problem of jammer selection (JS) for enhancing the secrecy goodput in a cooperative cognitive radio network with the multiple‐input–multiple‐output capability. First, they propose an optimal stopping theory‐based JS scheme in the presence of a single eavesdropper. The proposed scheme can accommodate the cases of beamforming or non‐beamforming jamming signals. Furthermore, in the presence of multiple eavesdroppers, they develop a random JS scheme with the beamforming design. Their theoretical analysis and simulation results demonstrate that the proposed schemes can effectively improve the secrecy goodput.

In this paper, we investigate the problem of jammer selection for securing Cooperative Cognitive Radio Networks (CCRNs) with the Multiple-Input Multiple- Output (MIMO) capability. In the CCRN under our consideration, there exist a pair of Primary Users (PUs), a relay node, a number of Secondary User (SU) pairs, and an eavesdropper. The PUs need to select a pair of SUs as jammers to interfere with the eavesdropper so as to preserve the secrecy of their wireless communications. To address this problem, we propose an Optimal Stopping based Jammer Selection (OSJS) scheme. Specifically, OSJS examines the primary secrecy capacity for each candidate SU pair in a sequential order. The first SU pair that makes the primary secrecy capacity higher than an optimal threshold is selected as the jammers. The optimal threshold is calculated based on the distribution function of the primary secrecy capacity. We derive the distribution function from the chi-square distribution function of the Signal-to-Noise Ratio (SNR) under the MIMO channel conditions. Since our OSJS scheme does not have to check all the candidate SU pairs, much time can be saved for data transmissions. Our rigorous analysis and simulation results demonstrate that our proposed scheme can achieve secure communications with improved network throughput.

Content-centric networking (CCN) adopts a receiver-driven, hop-by-hop transport approach that facilitates in-network caching, which in turn leads to multiple sources and multiple paths for transferring content. In such a case, keeping a single round trip time (RTT) estimator for a multi-path flow is insufficient as each path may experience different round trip times. To solve this problem, it has been proposed to use multiple RTT estimators to predict network condition. In this paper, we examine an alternative approach to this problem, CHoPCoP, which utilizes explicit congestion control to cope with the multiple-source, multiple-path situation. Protocol design innovations of CHoPCoP include a random early marking (REM) scheme that explicitly signals network congestion, and a per-hop fair share Interest shaping algorithm (FISP) and a receiver Interest control method (RIC) that regulate the Interest rates at routers and the receiver respectively. We have implemented CHoPCoP on the ORBIT testbed and conducted experiments under various network and traffic settings. The evaluation shows that CHoPCoP is a viable approach that can effectively deal with congestion in the multipath environment.

Intra-flow network coding (NC) is an innovative technique that has potential to improve multicast performance in wireless mesh networks (WMNs) by allowing intermediate forwarding nodes (FNs) to use coding and overhearing to reduce the number of required transmissions. However the benefits of the NC technology are limited unless there are protocols to exploit its capabilities. The existing intra-flow NC-based multicast protocols are all based upon the conventional end-to-end transport principle. By such a principle, intermediate FNs are unable to accurately determine the minimum number of coded packets they should transmit in order to ensure successful data delivery to the destinations, and hence redundant packets can be injected into the network, leading to performance degradation. Furthermore, the existing protocols cannot handle the bandwidth heterogeneity of multicast receivers very well. We argue that a receiver-driven hop-by-hop transport approach is more suitable for intra-flow NC and these two techniques can create synergy by enabling cooperation among the FNs. In this paper we propose HopCaster, a novel protocol that incorporates intra-flow NC with hop-by-hop transport to achieve high-throughput reliable multicast and to solve the heterogeneous receiver issue. It completely eliminates the need for estimating the number of coded packets to be transmitted by a FN and avoids transmission of redundant packets, as well as simplifies multicast management and congestion control. Moreover, HopCaster employs a cross-layer rate adaptation mechanism that optimizes radio transmission rate in hop-by-hop multicast by taking into consideration next-hop node population changes. Our evaluations show that HopCaster outperforms the existing NC-based reliable multicast protocol.

Delivery of high-quality streaming services over multi-hop wireless mesh networks (WMNs) is a challenging research problem because of quality fluctuation and interference of wireless links in WMNs, as well as strict throughput, delay, and reliability requirements of streaming applications. In this paper, we propose a Network Assisted Peer-to-Peer (NAP2P) system for file-based media streaming services such as video-on-demand in WMNs. In NAP2P, mesh routers dynamically cache content and form a P2P network with end user devices. This architecture enables several efficient and scalable communication mechanisms, for example, automatic caching and multi-source multi-path streaming for optimizing system performance. We address the design issues in NAP2P. Especially we design a multi-source multi-path routing mechanism to meet the QoS requirements of streaming sessions. A mathematical formulation for such source selection and routing optimization problem is presented, which determines the optimal content sources and streaming paths for multiple concurrent flows in the presence of wireless interference and subject to flow QoS constraints. By leveraging the unique architecture of NAP2P, we investigate the performance gain by applying mesh cache routers as network peers in P2P networks, as well as the benefits of allowing end user devices to provide content to their peers.

Recently, a new paradigm for cognitive radio networks has been advocated, where primary users (PUs) recruit some secondary users (SUs) to cooperatively relay the primary traffic. However, all existing work on such cooperative cognitive radio networks (CCRNs) operate in the temporal domain. The PU needs to give out a dedicated portion of channel access time to the SUs for transmitting the secondary data in exchange for the SUs' cooperation, which limits the performance of both PUs and SUs. On the other hand, Multiple Input Multiple Output (MIMO) enables transmission of multiple independent data streams and suppression of interference via beam-forming in the spatial domain over MIMO antenna elements to provide significant performance gains. Researches have not yet explored how to take advantage of the MIMO technique in CCRNs. In this paper, we propose a novel MIMO-CCRN framework, which enables the SUs to utilize the capability provided by the MIMO to cooperatively relay the traffic for the PUs while concurrently accessing the same channel to transmit their own traffic. We design the MIMO-CCRN architecture by considering both the temporal and spatial domains to improve spectrum efficiency. Further we provide theoretical analysis for the primary and secondary transmission rate under MIMO cooperation and then formulate an optimization model based on a Stackelberg game to maximize the utilities of PUs and SUs. Evaluation results show that both primary and secondary users achieve higher utility by leveraging MIMO spatial cooperation in MIMO-CCRN than with conventional schemes.

Mobile video broadcasting service, or mobile TV, is expected to become a popular application for 3G wireless network operators. Most existing solutions for video Broadcast Multicast Services (BCMCS) in 3G networks employ a single transmission rate to cover all viewers. The system-wide video quality of the cell is therefore throttled by a few viewers close to the boundary, and is far from reaching the social-optimum allowed by the radio resources available at the base station. In this paper, we propose a novel scalable video broadcast/multicast solution, SV-BCMCS, that efficiently integrates scalable video coding, 3G broadcast, and ad-hoc forwarding to balance the system-wide and worst-case video quality of all viewers at 3G cell. We solve the optimal resource allocation problem in SV-BCMCS and develop practical helper discovery and relay routing algorithms. Moreover, we analytically study the gain of using ad-hoc relay, in terms of users' effective distance to the base station. Through extensive real video sequence driven simulations, we show that SV-BCMCS significantly improves the system-wide perceived video quality. The users' average PSNR increases by as much as 1.70 dB with slight quality degradation for the few users close to the 3G cell boundary.

emerging technologies such as orthogonal frequency division multiplexing (OFDM) and multiple in multiple out (MIMO) has significantly increased the bandwidth of a wireless channel. A potential application of these technologies is to distribute digital video content over wireless in a home environment. However, video streaming is bandwidth demanding, and the number of interference free wireless channels is limited, selecting the best operating channel is critical to guarantee the quality of viewer's experience. We study the feasibility of transmitting video over WLANs in a multi-dwelling unit environment; design a centralized channel selection algorithm that satisfies each client's demand, at the same time, takes the fairness and residual bandwidth into consideration. We use directed edge-weighted graph to model the interference between different basic service systems and classify APs into cooperative APs and non cooperative APs. Our simulation results show that using existing 802.11a channels, a feasible channel allocation scheme for video streaming over WLANs in a dense deployment can be found in most cases.

Orthogonal frequency-division multiple access (OFDMA) exploits the frequency selective property of a wireless communication channel by allocating subchannels to individual users. Conventional multicast systems, however, suffer from diversified channel conditions among users. The main contribution of this paper is to maximize the multicast rate of one multicast session while being fair among users and build a general framework for resource allocation for multi multicast sessions. By assuming availability of channel state information, we study resource allocation in a multicast OFDMA system. We first study one multicast session system and solve the resource allocation problem in two steps. We first propose a novel subchannel allocation algorithm by allocating each subchannel to a group of users in the multicast session based on their subchannels gains. Second, a bit loading algorithm is also proposed. We further study multiple multicast transmission systems and propose a greedy algorithm for resource allocation to achieve proportional fairness among sessions. Finally, we numerically show that proposed algorithms outperform conventional multicast schemes and previous works for one multicast scenario and conventional multicast schemes for multi multicast scenario.

Wireless video multicast efficiently streams video to multiple receivers. When designing a wireless video multicast system, the system must be able to handle (i) packet losses induced by the underlying wireless channel and (ii) receiver heterogeneity in channel condition in a multicast group. We propose Layer Bargaining, a wireless video multicast design in infrastructure-based wireless networks that simultaneously addresses both of the above problems. To combat packet losses and improve received video quality, we propose a layered hybrid ARQ scheme that provides unequal protection to layered video by exploring light-weight feedback. To deal with receiver heterogeneity, we propose a framework based on Nash bargaining game for operating point selection in a multicast group. Our simulation results show that the layered hybrid ARQ scheme significantly outperforms the conventional hybrid ARQ scheme with single layer video and the layered FEC scheme. The results also show that the game-based operating point selection provides a high overall system performance while facilitating fairness among receivers. We carefully examine the overhead and the computational complexity of our proposed schemes, through both theoretical analysis and OPNET simulation/experiment, and show that Layer Bargaining is practically feasible under representative settings.

Mobile video broadcasting service, or mobile TV, is a promising application for 3G wireless network operators. Most existing solutions for video broadcast/multicast services in 3G networks employ a single transmission rate to cover all viewers. The system-wide video quality of the cell is therefore throttled by a few viewers close to the boundary, and is far from reaching the social-optimum allowed by the radio resources available at the base station. In this paper, we propose a novel scalable video broadcast/multicast solution, SV-BCMCS, that efficiently integrates scalable video coding, 3G broadcast and adhoc forwarding to balance the system-wide and worst-case video quality of all viewers in a 3G cell. We study the optimal resource allocation problem in SV-BCMCS and develop practical helper discovery and relay routing algorithms. Through analysis and extensive OPNET simulations, we demonstrate that SV-BCMCS can significantly improve the system-wide video quality at the price of slight quality degradation of a few viewers close to the boundary.

Multi-hop wireless mesh networks (WMNs) are emerging as a promising technology with applications in last mile Internet access, public safety, disaster response, battlefield, etc. The unique characteristics of WMNs such as limited and dynamic bandwidth, error prone nature, interferences, and user mobility, however, pose a number of significant challenges for high quality video streaming. In this paper, we discuss the major challenges, review some general approaches, and present a Unified Cache and Peer-to-Peer (UNICAP) framework we developed recently to support high quality streaming over WMNs. Issues addressed in UNICAP include the architecture, cache server/peer/route selection with cross-layer optimization, and admission control. Some future research directions are also discussed.

Supporting user interactivity such as VCR operations is desirable however it introduces extra complexity and overhead into the VoD system. VCR operations such as random seek, rewind, and fast forward change the location of viewing point, the video playback direction, and the video playback speed. In traditional client-server service model, the streaming server adjusts the streaming schedule on the fly and allocates extra server bandwidth to accomodate these interactive operations. In P2P VoD system, an overlay is established among server and users/peers. Peers pull the video content from the server and neighboring peers. The change of viewing location, direction, and speed not only affects the server but also other peers. The neighboring peers may not have the data at the new playback point, or sufficient uplink capacity to support faster playback rate. The available data at the requesting peer is changed due to VCR operations, which may affect neighboring peers' data downloading. In this paper, we investigate the feasibility of supporting VCR operations in PONDER, a mesh-based P2P VoD system. PONDER employs a dual approach that incorporates the mesh-based p2p downloading into the unicast-based VoD. To accommodate VCR operations, PONDER dynamically adjusts the downloading priority based on the interactivity requirements. The measurement-based admission control admits as many users as possible yet achieves good performance level in face of users' interactivities. Initial results show that PONDER can support VCR operations without degrading users' viewing quality.

In multi-hop infrastructure wireless mesh networks (WMNs), the association mechanism, by which a client station (STA) affiliates with a mesh access point (MAP), and the routing algorithm, through which MAPs form a multi-hop backhaul for relaying STAs' traffic, determine a two-tier logical topology. Apparently the STA-MAP association mechanism and the backhaul routing impact the available bandwidth that can be allocated to each STA. In this paper, we formulate a joint optimization problem of STA-MAP association, backhaul routing and bandwidth allocation. Our rigorous framework maximizes the network throughput while guaranteeing network-wide fairness among STAs, taking into account the bandwidth constraints of both access and backhaul links, as well as the wireless interference. We then develop approximation algorithms for efficiently solving the joint optimization problem. A method to decouple topology construction and bandwidth allocation is proposed to simplify the optimization problem under integral association and single- path routing, which is NP hard in the original formulation. We also use the clique approximation to alleviate the complexity for constructing the wireless interference constraints. Furthermore a scheduling algorithm is proposed, which coordinates channel access to provide bandwidth guarantee and can recover certain performance loss due to the clique approximation. Our evaluation demonstrates that constructing a good logical topology can improve throughput while enhancing fairness, and our algorithms can achieve performance close to the optimal solution to the joint association, routing and bandwidth allocation problem.

This paper proposes new MAC layer transmission opportunity (TXOP) adaptation algorithms for achieving temporal fairness in multi-rate 802.11 WLANs, which take underlying capture effect into account. Due to capture effect, a frame with the strongest received signal strength can be correctly decoded at the receiver even in the presence of transmission collisions from multiple contending stations. This effect introduces significant imbalance in channel access probabilities, and consequently the use of equal TXOP for each contending station cannot achieve temporal fairness. We develop a centralized and a distributed TXOP adaptation algorithm that compensate the stations with less channel access opportunities by giving them larger TXOPs. In the proposed centralized scheme, the access point estimates the successful TXOP acquisition probability of each associated station and allocates appropriate TXOPs to the contending stations. In the proposed distributed algorithm, each station estimates its own share of channel occupation time and adjusts its TXOP individually. We present the conditions that ensure the convergence of the distributed algorithm. Simulation results show that our proposed schemes can effectively achieve "true" temporal fairness.

In this paper, we present UPAC, a unified peer-to-peer (P2P) and cache framework for high quality video-on-demand services over infrastructure multi-hop wireless mesh networks. Streaming video in multi-hop wireless networks faces many challenges, e.g., the varying available path bandwidth, interference due to shared medium, the impact of multiple relay nodes, etc. To increase the capacity of streaming services and ensure high video quality, in UPAC, the video content is cached at selected wireless mesh access points (MAPs) inside the mesh network. Furthermore peers help each other on video downloading in a best effort manner to reduce the workload imposed on the servers and networks. This unified framework has the advantages of both the content distribution network approach and peer-to-peer network approach. To obtain optimal video quality, a user device can form the P2P relationship with MAP content cache servers and other user devices. Meanwhile, the client-server relationship is established between the device and the MAP content cache servers. In addition, we propose and compare several methods to select the content cache servers for serving a device and to establish the end-to-end routes between the device and the selected servers. Our preliminary simulation results show that the proposed framework increases the number of users that can be concurrently served in the infrastructure wireless mesh network and improves the received video quality.

In multicast/broadcast services over infrastructure- based/cellular wireless networks (e.g. 3G cellular networks, WiMax, DVB), data is transmitted to multiple recipients from an access point/base station. Multicast greatly improves the network efficiency to distribute data to multiple recipients as compared to multiple unicast sessions of the same data to each receiver individually, by taking advantage of the shared nature of the wireless medium. However it is difficult to guarantee the reception reliability of multiple multicast/broadcast recipients because the wireless medium is error prone and each receiver experiences different channel conditions. An additional difficulty is that multicast/broadcast services in many networks such as 3G multimedia multicast services do not provide a reverse communications channel for the receivers to request the retransmission of lost data packets. This research proposes a novel method to provide QoS support by using an assistant network to recover the loss of multicast data in the principal network. Wireless devices are connected to the principal network to receive the multicast data. A wireless device may lose some of the multicast data sent over the principal network. The wireless devices form an assistant network to recover the lost multicast data cooperatively from their peers. The performance of this recovery mechanism has been investigated using extensive simulation experiments.

In a wireless mesh network, a client station needs to associate with a mesh access point for network access. Conventional association mechanisms assume a high-speed backhaul and only the access link being the bottleneck. This assumption holds for most WLANs, but in wireless mesh networks traffic could be bottlenecked either by the access link or by the bandwidth-limited wireless backhaul. In this paper, we propose an association mechanism for wireless mesh networks to improve stations' end- to-end communication performance with the Internet. A station makes its association decision by jointly considering the quality of the access link between the station and the candidate mesh access point as well as the cost of the multi-hop path from the mesh access point to the gateway. In addition, we design two access link metrics, Contention Aware Expected Transmission Time (CAETT) and Load Aware Expected Transmission Time (LAETT). The main strength of CAETT is incorporating the impact of 802.11 MAC layer contention on the bandwidth sharing among the multi-rate stations. LAETT further captures the traffic load. We evaluate the performance of our system through simulations and demonstrate that the proposed joint association mechanism with the CAETT/LAETT metric can significantly improve the end-to-end performance for wireless mesh networks by up to 60%.

We report the design, implementation and evaluation of Merged Hybrid ARQ with staggered FEC (MHARQ) system for video multicast over wireless LANs. MHARQ combines the advantages of receiver-driven staggered FEC and hybrid ARQ schemes to compensate the large dynamic range of WLAN channels and to achieve high reliability, scalability and wireless bandwidth efficiency for video multicast. The FEC packets generated by a cross-packet FEC code are divided into multiple streams according to the pre-configured overhead and are transmitted in different multiple IP multicast groups. Certain FEC streams are delayed from the original video stream. The receivers dynamically join/leave the FEC multicast groups based on the channel conditions. For efficient utilization of WLAN bandwidth, FEC data for a multicast group would not be transmitted by the APs in wireless networks if no receiver joins this group. The time shift between the video stream and the FEC streams introduces temporal diversity and compensates for the client join delay and handoff interruption. In addition, when delayed FEC packets are not enough to recover the lost packets, the receivers can send a hybrid ARQ request to the video server. We design a channel estimation algorithm for a receiver to dynamically determine the delayed FEC multicast groups to join and/or send ARQ NACK to request for retransmission. Using the ORBIT radio grid testbed, we have investigated the performance of the proposed MHARQ system with various numbers of users per AP and different number of APs per video server. It is demonstrated via real system implementation on ORBIT that MHARQ improves wireless bandwidth efficiency and scalability for reliable video multicast, compared with existing reliable multicast schemes.

Streaming video over wireless mesh networks is an attractive and challenging technology. It is important to select an appropriate metric in the routing algorithm to improve the video delivery quality. In this paper, we report the design, implementation, and evaluation of a prototype for video streaming over a wireless mesh network. In order to select the best path dynamically to improve the multimedia performance, Ad Hoc On-Demand Distance Vector (AODV) routing protocol is enhanced with a new radio and bandwidth aware routing metric, better route request/reply message process rules and periodic route refreshment and maintenance. A metric quantization method is also proposed to maintain the route stability while achieving quick response to the network dynamics. In addition, we implemented a proxy function in the mesh access point so that the stations can access the mesh through the access point without any modification to themselves. Using the prototype, we prove the concept that our approach can significantly improve the video quality over the traditional AODV routing protocol in a real system implementation.

We report the implementation experience and experimental evaluation of a staggered adaptive forward error correction (FEC) system for video multicast over wireless LANs. In the system, the parity packets generated by a cross-packet FEC code are transmitted at a time delay from the original video packets, i.e. staggercasting video stream and FEC stream in different multicast groups. The delay provides temporal diversity to improve the robustness of video multicast, especially to enable the clients to correct burst packet loss using FEC and to achieve seamless handoff. A wireless client dynamically joins the FEC multicast groups based upon its channel conditions and handoff events. We have implemented the system including the streaming server and client proxy. A novel software architecture is designed to integrate the FEC functionality in the clients without requirement for changing the existing video player software. We conduct extensive experiments to investigate the impact of FEC overhead and the delay between the video stream and FEC stream to the video quality under different interference levels and mobile handoff durations. The efficacy of staggered adaptive FEC system on improving video multicast quality is demonstrated in real system implementation.

Compared with unicast, video multicast over 802.11 WLANs should handle varying channel conditions of multiple users and user topology changes as well as scalability to achieve good quality for all users in the serving area. This paper analyzes error control strategies available in different layers of the network stack, including modulation and channel coding in physical layer, cross-packet Forward Error Correction (FEC), packet size optimization and scalable video coding in application layer. By combining and adapting these schemes jointly, an adaptive cross layer optimization algorithm is proposed for scalable video multicast over 802.11 WLANs. Based on a variety of criteria, improvement in overall video quality for all the targeted users can be achieved.

This paper describes cross-layer optimization strategies and simulation results for H.264 videomulticast over wireless LAN. The proposed scheme takes into account the varying channe conditions of multiple users, and dynamically allocates available bandwidth between source coding and channel coding. In particular, source coding parameters (intra update and quantization) and application-layer FEC code rate are chosen jointly to optimize a multicast performance criterion, based on feedbacks from all multicast receivers. Two performance criteria for video multicast are investigated and compared.

This paper presents an overview of the ORBIT (open access research testbed for next-generation wireless networks) radio grid testbed, that is currently being developed for scalable and reproducible evaluation of next-generation wireless network protocols. The ORBIT testbed consists of an indoor radio grid emulator for controlled experimentation and an outdoor field trial network for end-user evaluations in real-world settings. The radio grid system architecture is described in further detail, including an identification of key hardware and software components. Software design considerations are discussed for the open-access radio node, and for the system-level controller that handles management and control. The process of specifying and running experiments on the ORBIT testbed is explained using simple examples. Experimental scripts and sample results are also provided.