Cross-layer Optimization Framework Research Articles

Joint Power Control and Rate Adaptation for Video Streaming in Wireless Networks With Time-Varying Interference

This paper considers a cross-layer optimization framework for video streaming in multinode wireless networks with a time-varying interference environment. We develop a distributed joint power control and rate adaptation framework that exploits the time diversity of the wireless channels, satisfies the hard delay constraints associated with video applications, and respects a certain fairness criterion among the nodes. The proposed framework performs power allocation at the physical/media access control layers to achieve a certain target signal-to-interference-plus-noise ratio, such that the difference between the arrival and the departure rates at the queues is very small, and performs video rate adaptation at the video coding layer (upper layer) according to the nodes' demanded video quality, their channel conditions, and a given fairness criterion. A main challenge here is that the adaptation of the video rate and the power control is not performed at the same time scale. We deal with this issue and model the power and rate variations of the nodes as linear stochastic dynamic equations and formulate a risk-sensitive control problem that captures the hard delay constraints of the video services and the fairness criterion for resource utilization. We provide an optimal solution for this control problem and illustrate the performance of our framework through simulations.

Heterogeneous cognitive networks: spectrum sharing with adaptive opportunistic DSMA for collaborative PCP-OFDM system

To allow secondary and primary concurrent transmissions achieving optimal spectrum utilization in heterogeneous cognitive networks (HCN), this paper proposes a cross-layer optimization framework with a distributed cooperative spectrum sharing methodology using adaptive opportunistic DSMA (DAO-DSMA) scheme when multiple SUs use part of the bandwidth in PCP-OFDM system to collaboratively augment spectrum reuse gain to automatically select the PU's radio bands and the SU's operating modes. The cases where nodes cooperate in a distributed way to maximize the TCP throughput or to achieve either proportional fair or max---min fair medium access are considered. A gripping slant of DAO-DSMA regime is the benefit to derive the probability transition matrix for the Markov chain of secondary users performing Adaptive Markov Prediction in Dynamic Spectrum Access. Simulation results illustrate that the proposed scheme investigates characteristic changes of different parameters on transmission capacities of the coexisting network and makes great improvement on the ergodic capacity of secondary user to obtain better performance of spectrum coexistence by maintaining overall maximum TCP throughput.

Cross-layer optimization with MIPv6-based multiple mobile routers for cognitive networks

To allow secondary and primary concurrent transmissions achieving the increment of optimal mobile network performance in heterogeneous cognitive networks, this paper proposes a cross-layer optimization framework with a distributed cooperative MIPv6-based multiple mobile routers using adaptive opportunistic route optimization entitled Mobile IPv6 Route Optimization for Cognitive radio networks (MIROC) protocol. A gripping slant of MIROC protocol is the benefit to develop mobile cognitive routers based on MIPv6. The proposed protocol is elaborated in details from angular RO and multiangular routing which can be utilized in practical network deployment. The performance in M/M/n and M/M/? Queue and evaluation of cognitive mobile Network with multiple mobile routers using stochastic process in Markov chain model are analyzed.

A genetic approach on cross-layer optimization for cognitive radio wireless mesh network under SINR model

Due to the limited spectrum resources and the differences of link loads, how to obtain maximum network throughput through cross-layer design under signal-to-interference-and-noise ratio (SINR) model is recognized as a fundamental but hard problem. For this reason, the throughput maximization problem jointly with power control, channel allocation and routing under SINR model is researched. First, by formulating the optimization model and digging up its special structure, we show that the throughput maximization problem can be decomposed into two sub-problems: a channel allocation and power control sub-problem at the link-physical layer, and a throughput optimization sub-problem at the network layer. As to the link-physical layer sub-problem, since the joint optimization on channel allocation and power control is NP hard, we apply genetic algorithm for searching the optimal solution. As to the network layer sub-problem, we use linear programming technique for throughput optimization. To reflect the interplay property among these three layers, the fitness of each individual in the genetic algorithm is evaluated by solving the network layer sub-problem. Therefore, an effective cross-layer optimization framework based on genetic algorithm is obtained, which can find optimized power control, channel allocation and route selection in polynomial time. In order to enhance the convergence process during evolution, the integer based representation scheme and corresponding genetic operators are well designed with appropriate constraint control mechanisms. Extensive simulation results demonstrate that the proposed scheme obtains higher network throughput compared to the pervious works with comparable computational complexity.

Adaptive Resource Allocation for MC-CDMA and OFDMA in Reconfigurable Radio Systems

This paper studies the uplink resource allocation for multiple radio access (MRA) in reconfigurable radio systems, where multiple-input and multiple-output (MIMO) multicarrier-code division multiple access (MC-CDMA) and MIMO orthogonal frequency-division multiple access (OFDMA) networks coexist. By assuming multi-radio user equipment with network-guided operation, the optimal resource allocation for MRA is analyzed as a cross-layer optimization framework with and without fairness consideration to maximize the uplink sum-rate capacity. Numerical results reveal that parallel MRA, which uses MC-CDMA and OFDMA networks concurrently, outperforms the performance of each MC-CDMA and OFDMA network by exploiting the multiuser selection diversity.

Cross-Layer Provision of Future Cellular Networks: A WMMSE-based approach

To cope with the growing demand for wireless data and to extend service coverage, future fifth-generation (5G) networks will increasingly rely on the use of low-power nodes to support massive connectivity in a diverse set of applications and services [1]. To this end, virtualized and mass-scale cloud architectures are proposed as promising technologies for 5G in which all the nodes are connected via a backhaul network and managed centrally by such cloud centers. The significant computing power made available by the cloud technologies has enabled the implementation of sophisticated signal processing algorithms, especially by way of parallel processing, for both interference management and network provision. The latter two are among the major signal processing tasks for 5G due to an increased level of frequency sharing, node density, interference, and network congestion. This article outlines several theoretical and practical aspects of joint interference management and network provisioning for future 5G networks. A cross-layer optimization framework is proposed for joint user admission, user-base station (BS) association, power control, user grouping, transceiver design, as well as routing and flow control. We show that many of these cross-layer tasks can be treated in a unified way and implemented in a parallel manner using an efficient algorithmic framework called weighted minimum mean squared error (WMMSE). Some recent developments in this area are highlighted and future research directions are identified.

Exploiting social features for improving cognitive radio infrastructures and social services via combined MRF and back pressure cross-layer resource allocation

The observed unbalance of spectrum utilization combined with the proliferation of wireless devices and the growth of online social networks call for a joint study of cognitive radios (CRs) and social networking towards improving the operation of both the underlying CR-capable communications infrastructures and of the corresponding dynamic networks. In this work, we address such issue and focus on the twofold direction of resource allocation in CRs and social feature exploitation with the aim of providing a holistic cross-layer optimization framework that jointly considers information from the social layer as well as the traditional network protocol layers. We combine Markov random field (MRF) cross-layer decisions with back pressure (BP) features for the allocation of resources, in order to improve the operation of CRs that act as social network substrate. Espousing the recent advances in the field of dynamic spectrum allocation, the proposed BP-enhanced MRF (BPeMRF) network optimization serves the purpose of improving in a low complexity manner the capacity of agile infrastructures, which are required to adapt fast according to the demands of the volatile CR environment and the overlaying dynamic online social networks. In addition, within the proposed socially enhanced BPeMRF (SeBPeMRF) approach, we leverage emerging social information to further enhance performance. The obtained results exhibit the efficacy of BPeMRF/SeBPeMRF regarding the above objectives and demonstrate significant promise for further improving the corresponding infrastructures and services.

Distributed Stochastic Cross-Layer Optimization for Multi-Hop Wireless Networks With Cooperative Communications

Cooperative communication has been shown to have great potential in improving wireless link quality. Incorporating cooperative communications in multi-hop wireless networks has been attracting a growing interest. However, most current research focuses on either centralized solutions or schemes limited to specific network problems. In this paper, we propose a distributed framework that uses Network Utility Maximization (NUM) to optimize the following joint objectives: flow control, routing, scheduling, and relay assignment; for multi-hop wireless cooperative networks with general flow and cooperative relay patterns. We define two special graphs, Hyper Forwarding Graphs (HFG) and Hyper Conflict Graphs (HCG), to represent all possible cooperative routing policies and interference relations among the cooperative relays respectively. Based on HFG and HCG, a stochastic mixed-integer non-linear programming problem is formulated. We then propose lightweight algorithms to solve these in a fully distributed manner, and derive the theoretical performance bounds of these proposed algorithms. Simulation results verify our theoretical analysis and reveal the significant performance gains of our framework, in terms of throughput, flexibility, and scalability. To our knowledge, this is the first distributed cross-layer optimization framework for multi-hop wireless cooperative networks with general flow and cooperative relay patterns.

Joint Optimization of User-Experience and Energy-Efficiency in Wireless Multimedia Broadcast

This paper presents a novel cross-layer optimization framework to improve the quality of user experience (QoE) and energy efficiency of the heterogeneous wireless multimedia broadcast receivers. This joint optimization is achieved by grouping the users based on their device capabilities and estimated channel conditions experienced by them and broadcasting adaptive content to these groups. The adaptive multimedia content is obtained by using scalable video coding (SVC) with optimal source encoding parameters resulted from an innovative cooperative game. Energy saving at user terminals results from using a layer-aware time slicing approach in the transmission stage. A trade-off between energy saving and QoE is observed, and is incorporated in the definition of a utility function of the players in the formulated heterogeneous user composition and physical channel aware game. An adaptive modulation and coding scheme is also optimally incorporated in order to maximize the reception quality of the broadcast receivers, while maximizing the network broadcast capacity. Compared to the conventional broadcast schemes, the proposed framework shows an appreciable improvement in QoE levels for all users, while achieving higher energy-savings for the energy constrained users.

Distortion-Fair Cross-Layer Resource Allocation for Scalable Video Transmission in OFDMA Wireless Networks

The design of optimized video delivery to multiple users over a wireless channel is a challenging task, especially when the objectives of maximizing the spectral efficiency and providing a fair video quality have to be jointly considered. In this paper we propose a novel cross-layer optimization framework for scalable video delivery over OFDMA wireless networks. It jointly addresses rate adaptation and resource allocation with the aim of maximizing the sum of the achievable rates while minimizing the distortion difference among multiple videos. After having discussed the feasibility of the optimization problem, we consider a “vertical” decomposition of it and propose the iterative local approximation (ILA) algorithm to derive the optimal solution. The ILA algorithm requires a limited information exchange between the application and the MAC layers, which independently run algorithms that handle parameters and constraints characteristic of a single layer. In order to reduce the overall complexity and the latency of the optimal algorithm, we also propose suboptimal strategies based on the first-step of the ILA algorithm and on the use of stochastic approximations at the MAC layer. Our numerical evaluations show the fast convergence of the ILA algorithm and the resulting small gap in terms of efficiency and video quality fairness between optimal and suboptimal strategies. Moreover, significant individual PSNR gains, up to 7 dB for high-complexity videos in the investigated scenario, are obtained with respect to other state-of-the-art frameworks with similar complexity.

An Improved Autonomous Cross-Layer Optimization Framework for Wireless Multimedia Communication in Heterogeneous Networks

Various cross-layer optimization schemes have been proposed for the purpose of enhancing wireless network performances. In this paper, we present an improved autonomous cross-layer optimization to fit modern heterogeneous communication networks. In our design, different Quality of Service (QoS) requirements for all sorts of information with various communication protocols are considered when making optimal transmission decisions. The modified framework achieves better system computation efficiency and adapts to various sources of different communication networks quickly while preserves the overall system performance.

Crowdsourcing in Cyber-Physical Systems: Stochastic Optimization With Strong Stability

Cyber-physical systems (CPSs), featuring a tight combination of computational and physical elements as well as communication networks, attracted intensive attention recently because of their wide applications in various areas. In many applications, especially those aggregating or processing a large amount of data over large spatial regions or long spans of time or both, the workload would be too heavy for any CPS element (or node) to finish on its own. How to enable the CPS nodes to efficiently collaborate with each other to accommodate more CPS services is a very challenging problem and deserves systematic research. In this paper, we present a cross-layer optimization framework for hybrid crowdsourcing in the CPSs to facilitate heavy-duty computation. Particularly, by joint computing resource management, routing, and link scheduling, we formulate an offline finite-queue-aware CPS service maximization problem to crowdsource nodes' computing tasks in a CPS. We then find both lower and upper bounds on the optimal result of the problem. In addition, the lower bound result is proved to be a feasible result that guarantees all queues in the network are finite, i.e., network strong stability. Extensive simulations have been conducted to validate the proposed algorithms' performance.

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.

Robust cross layer optimization in relay aided cellular networks

A robust optimization framework for cross layer optimization is proposed to deal with time varying channels, reducing the required channel state feedback overhead and giving strong QoS guarantees. Lower bounds for the achieved goodput when robust scheduling is used are derived and through simulation we study the impact in terms of blocked calls and QoS achieved by the robust solution for two different cell topology layouts: when 2-hop relaying is allowed and when only direct base station to user transmissions are allowed. The results reveal that the price of robustness is lower for the 2-hop case than the single hop case.

Statistical Routing for Multihop Wireless Cognitive Networks

To account for the randomness of propagation channels and interference levels in hierarchical spectrum sharing, a novel approach to multihop routing is introduced for cognitive random access networks, whereby packets are randomly routed according to outage probabilities. Leveraging channel and interference level statistics, the resultant cross-layer optimization framework provides optimal routes, transmission probabilities, and transmit-powers, thus enabling cognizant adaptation of routing, medium access, and physical layer parameters to the propagation environment. The associated optimization problem is non-convex, and hence hard to solve in general. Nevertheless, a successive convex approximation approach is adopted to efficiently find a Karush-Kuhn-Tucker solution. Augmented Lagrangian and primal decomposition methods are employed to develop a distributed algorithm, which also lends itself to online implementation. Enticingly, the fresh look advocated here permeates benefits also to conventional multihop wireless networks in the presence of channel uncertainty.

A Cross-Layer Design for Perceptual Optimization Of H.264/SVC with Unequal Error Protection

Delivering high perceptual quality video over wireless channels is challenging due to the changing channel quality and the variations in the importance of one source packet to the next for the end-user's perceptual experience. Leveraging perceptual metrics in concert with link adaptation to maximize perceptual quality and satisfy real-time delay constraints is largely unexplored. We introduce an APP/MAC/PHY cross-layer architecture that enables optimizing perceptual quality for delay-constrained scalable video transmission. We propose an online QoS-to-QoE mapping technique to quantify the loss visibility of packets from each video layer using the ACK history and perceptual metrics. At the PHY layer, we develop a link adaptation technique that uses the QoS-to-QoE mapping to provide perceptually-optimized unequal error protection per layer according to packet loss visibility. At the APP layer, the source rate is adapted by selecting the set of temporal and quality layers to be transmitted based on the channel statistics, source rates, and playback buffer state. The proposed cross-layer optimization framework allows the channel to adapt at a faster time scale than the video codec. Furthermore, it provides a tradeoff between playback buffer occupancy and perceptual quality. We show that the proposed architecture prevents playback buffer starvation, provides immunity against short-term channel fluctuations, regulates the buffer size, and achieves a 30% increase in video capacity versus throughput-optimal link adaptation.

Analytical Modeling for Delay-Sensitive Video Over WLAN

Delay-sensitive video transmission over IEEE 802.11 wireless local area networks (WLANs) is analyzed in a cross-layer optimization framework. The effect of delay constraint on the quality of received packets is studied by analyzing “expired-time packet discard rate”. Three analytical models are examined and it is shown that M/M/1 model is quite an adequate model for analyzing delay-limited applications such as live video transmission over WLAN. The optimal MAC retry limit corresponding to the minimum “total packet loss rate” is derived by exploiting both mathematical analysis and NS-2 simulations. We have shown that there is an interaction between "packet overflow drop" and "expired-time packet discard" processes in the queue. Subsequently, by introducing the concept of virtual buffer size, we will obtain the optimal buffer size in order to avoid "packet overflow drop". We finally introduced a simple and yet effective real-time algorithm for retry-limit adaptation over IEEE 802.11 MAC in order to maintain a loss protection for delay-critical video traffic transmission, and showed that the average link-layer throughput can be improved by using our adaptive scheme.

Video summary delivery over cooperative wireless networks

This article addresses cooperative video summary delivery over wireless networks which inherits advantages from both cooperative communication and video summarization. In this work, a novel decode-process-and-forward (DPF) scheme is proposed for video summary transmission, where a relay node with video processing capability is involved to generate a concise version of the summary frame, called summary of summary (SoS). The SoS information is effectively consumed by the destination side to enhance its error concealment capability, leading to an improved video reconstruction quality. This article proposes a generic crosslayer optimization framework for cooperative video summary transmission, which jointly considers the source coding, relay processing parameters, power allocation, and error concealment strategy to achieve the best video quality. The problem is solved by using Lagrangian relaxation and dynamic programming. Experimental results show that the proposed scheme significantly outperforms existing cooperative and non-cooperative transmission schemes.

Cross-Layer Optimization Using Advanced Physical Layer Techniques in Wireless Mesh Networks

The objective of this paper is to study the impact of advanced physical layer techniques on the maximum achievable throughput of wireless multihop mesh networks. We formulate a cross-layer optimization framework for the routing and scheduling problem jointly with the following physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding and their combinations. In the case when each node is enabled with superposition coding, we need to formulate a power allocation subproblem for the optimal power partition of the superimposed signals. We solve these joint problems exactly to compute the maximum achievable throughput in realistic size networks. This allows us to quantify the performance gains obtained by using these techniques (and their combinations). Specifically, we find that the use of dirty-paper coding (only at the gateway) is not justified in networks with mixed uplink and downlink flows. On the other hand, the combination of superposition coding with successive interference outperforms significantly other techniques across all transmission power range for both uplink and downlink flows. We also provide a number of interesting practical insights on throughput improvement by comparing different combinations of these techniques.

Cross-layer optimization for wireless mesh networks with smart antennas

A cross-layer optimization framework for wireless mesh networks is presented where at each node, various smart antenna techniques such as beam-forming, spatial division multiple access and spatial division multiplexing are employed. These techniques provide interference suppression, capability for simultaneous communication with several nodes and transmission with higher data rates, respectively, through multiple antennas. By integrating different combinations of the multi-antenna techniques in physical layer with various constraints from MAC and network layers, three Mixed Integer Linear Programming (MILP) models are presented to minimize the system activation time. Since these optimization problems are complex combinatorial, the optimal solution is approached by a Column Generation decomposition method. The numerical results for different network scenarios with various node densities, number of antennas, transmission ranges and number of sessions are provided. It is shown that the resulted directive, multiple access and multiplexing gains combined with scheduling, effectively increase both the spectrum spatial reuse and the capacity of the links and therefore, enhance the achievable system throughput. Our cross-layer approach is also extended to consider heterogeneous networks and we present a multi-criteria optimization framework to model the design problem where the objective is to jointly minimize the cost of deployment and the system activation time. Our results reveal the benefits of joint design in terms of reducing the cost of deployment while achieving higher system performance.