Content-aware rate allocation for efficient video streaming via dynamic network utility maximization

Nowadays it is vital to design robust mechanisms to provide QoS for multimedia applications as an integral part of the network traffic. The main goal of this paper is to provide an efficient rate control scheme to support content-aware video transmission mechanism with buffer underflow avoidance at the receiver in congested networks. Towards this, we introduce a content-aware time-varying utility function, in which the quality impact of video content is incorporated into its mathematical expression. Moreover, we analytically model the buffer requirements of video sources in two ways: first as constraints of the optimization problem to guarantee a minimum rate demand for each source, and second as a penalty function embedded as part of the objective function attempting to achieve the highest possible rate for each source. Then, using the proposed analytical model, we formulate a dynamic network utility maximization problem, which aims to maximize the aggregate hybrid objective function of sources subject to capacity and buffer constraints. Finally, using primal–dual method, we solve DNUM problem and propose a distributed algorithm called CA-DNUM that optimally allocates the shared bandwidth to video streams. The experimental results demonstrate the efficacy and performance improvement of the proposed content-aware rate allocation algorithm for video sources in different scenarios.     

面向智慧教室的无线传感网边缘节点智能部署方法

摘 要: 为降低部署后的通信时延,提高智慧教室的数据发送与网络使用效率,提出面向智慧教室的无线传感网边缘节点智能部署方法。以智慧教室场景中良好的通信、最大限度降低部署边缘节点成本为优化目标,构建边缘节点智能部署的目标函数。针对目标函数设定流量约束条件、无线传感网数据流约束条件、节点计算能力约束条件。自适应调整粒子群优化算法的惯性权重、粒子更新速度、Pareto最优解保存策略,设计多目标改进粒子群优化算法求解目标函数,实现面向智慧教室的无线传感网边缘节点智能部署。测试结果表明,该方法的时延较低,网络计算能力较高,保证了智慧教室无线传感网通信和传输质量。     

基于Lyapunov优化的时变无线传感网路由研究

针对无线传感网应用中监测环境具有随机性和不可预测性等因素使得节点感知速率通常是时变的且某些时刻会超出链路容量的实际问题,设计了一种时变路由算法.在该算法中,将时变感知速率下的路由问题建立成以时均的网络能耗与丢弃感知数据代价的加权和最小为目标的随机优化模型,并利用Lyapunov优化技术求解该模型,进而得到一种路由策略来实时决策每条链路上的数据流量以及由于节点感知速率持续超出链路容量而不得不丢弃的数据量.进一步,讨论感知数据不被丢弃的条件,建立目标函数与感知信息最大传输时延之间的权衡关系.最后,通过仿真实验,验证了本文算法在能耗、感知数据的丢弃量及传输时延之间的均衡关系.还在不同的最大数据感知速率下,比较了本文算法与AVE算法的性能.     

面向MANET云平台应用的多目标蚁群优化AODV路由协议

为增强网络中路由的稳健性,提出了一种优化的AODV路由协议,结合多目标优化和蚁群优化选择最优路由。首先,计算各节点的传输距离、进展、传输延迟、方向和生存时间5个度量;然后,以传输距离、传输延迟和方向3个度量的最小化,以及进展和生存时间两个度量的最大化为优化目标,构造蚁群算法中的移动概率函数,并结合局部最优路径更新全局信息素;此后,在AODV路由协议的基础上,选择移动概率最大的节点作为最佳下一跳节点,生成最优路由。最后,在部署的大学区实验云模型平台上进行了验证。仿真结果表明,与AODV和EN-AODV路由协议相比,新路由协议的报文送达率更高,同时端到端的平均时延和路由开销更小。     

OFDMA 协同网络中基于QoS 保证的资源分配算法

针对正交频分多址(OFDMA)协作通信系统,为了解决数据传输速率最大化并提供尽力而为业务与实时业务的服务质量(QoS)保证问题,提出新型资源分配算法.通过以总功率受限为约束条件,定义基于数据速率、时延和丢包率的效用函数,并以在协作传输中以最大化效用函数为目标进行中继选择和子载波分配.通过中继和用户上的子载波和功率分配方案的设计,从而最大化网络数据传输速率并最小化时延与丢包率,为多种业务提供服务质量保证.采用最优化理论与方法求解效用函数,得到了资源分配结果.仿真结果验证了算法收敛,并给出功率分配结果,以及网络吞吐量和时延性能指标情况,验证了算法的有效性.     

A new joint strategy of radio channel allocation and power control for wireless mesh networks

For wireless mesh networks, it is critical to allocate the limited number of radio channels efficiently while mitigating the co-channel inference to improve network performance. For this reason, after extensively reviewing the related work, we present a new joint radio channel allocation (RCA) and power control (PC) strategy for wireless mesh networks. First, we formulate the RCA problem as a multiple objective optimization problem, with the constraints of transmission power and traffic data rates, while effective channel utilization (ECU) is chosen as the target metric for optimization. Second, we incorporate the channel status into the model of MAC protocols, including as signal-to-noise ratio (SNR) and transmission power. Consequently, the PC is incorporated into the ECU for channel allocation. Third, we propose to directly maximize the ECU to find both optimal radio channel and transmission power. The resulting strategy is a fully distributed RCA/PC algorithm without relying on a coordination mechanism among mesh routers. Our extensive simulation results have demonstrated that the proposed algorithm significantly outperforms the existing RCA strategies and the standard MAC protocols in performance such as throughput, packet dropping, delay and delay jitter.     

异步粒子群优化算法在QoS组播路由中的应用

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。为适应下一代IP网络对实时信息传输的要求,在异步模式粒子群优化算法基础上,给出包含延迟、延迟抖动、带宽、丢包率和最小花费5个约束条件在内的QoS组播路由算法。该算法首先给出数学模型,设计适应度函数,再给出受限的网络模型,通过粒子群优化(PSO)算法最大化适应度函数来求解最优Steiner树。算法仿真实验结果表明:与遗传算法和同步模式的粒子群优化算法相比,该算法有较好的收敛速度和寻优效果。     

A framework of cross-layer design for multiple video streams in wireless mesh networks

In this paper, a novel cross-layer design framework for multiple realtime video traffics in CDMA wireless mesh networks is proposed. First, the performances of application, physical, MAC, and network layers are modeled by some classical models under reasonable assumptions. Then, we present a framework in which source coding, power control, ARQ control, and delay partitioning functionalities at different layers can be jointly optimized. Our objective is to maximize the video quality under strict end-to-end delay constraints through adjusting source coding rate, end-to-end delay distribution, and each node’s transmit power. This optimization problem is proved to be a nonlinear but log-convex one. Finally, we propose a centralized solution based on the classical convex programming method, as well as a partially distributed solution based on the Lagrangian dual decomposition technique. The both solutions are proved to converge to the global optimum of the above problem.     

一种联合多信道分配决策的认知Mesh系统数据传输优化算法

在认知Mesh系统进行数据传输的过程中,为了提高数据包投递成功率及网络的吞吐量,减少网络延迟时间,提出一种联合多信道分配决策的认知Mesh系统数据传输优化算法(JCWN)。针对信道的干扰问题,建立了认知Mesh系统的干扰无向图,分析节点链路的网络干扰电平。在节点的路由请求阶段通过提出基于信道干扰电平的路由指标函数,并通过权重阈值来为节点链路分配干扰较小的信道。在路由选择上,联合多路由算法计算每条路由路径的信道干扰程度,为了保障节点传输数据的成功率而选择干扰程度更小的路由。实验仿真结果表明,在数据包投递成功率上,该算法相比POC算法以及基于RL的算法提高了20%以上,在提高网络吞吐量,减少延迟时间上也表现出了更好地效果。     

基于改进萤火虫群优化的无线自组网路由算法

针对无线自组网络拓扑结构多变、网络生存时间受限及数据包分组传输效率低下等问题,借鉴萤火虫群优化算法,提出了一种改进萤火虫群优化的无线自组网络路由算法.路由算法将萤火虫优化算法中的荧光素强度更新与无线自组网络中的节点移动速度、拥塞程度、节点剩余能量、节点间距离等因素进行相互映射,同时改进萤火虫群优化算法中的搜索萤火虫、驻留萤火虫及回溯萤火虫用于完成无线自组网络中路由协议的路由发现、路由选择及路由维护等过程,整个协议无须传送大量的控制分组,即可实现无线自组网络的稳定传输.仿真实验结果表明,与AODV及基于蚁群优化的路由算法AntRouting协议相比,本文所提出的路由算法在端到端延时、分组数据传输率及网络生存时间上均有良好的性能.     

Error-Resilient Video Encoding and Transmission in Multirate Wireless LANs

In this paper, we present a cross-layer approach for video transmission in wireless LANs that employs joint source and application-layer channel coding, together with rate adaptation at the wireless physical layer (PHY). While the purpose of adopting PHY rate adaptation in modern wireless LANs like the IEEE 802.11a/b is to maximize the throughput, in this paper we exploit this feature to increase the robustness of wireless video. More specifically, we investigate the impact of adapting the PHY transmission rate, thus changing the throughput and packet loss channel characteristics, on the rate-distortion performance of a transmitted video sequence. To evaluate the video quality at the decoder, we develop a cross-layer modeling framework that considers jointly the effect of application-layer joint source-channel coding (JSCC), error concealment, and the PHY transmission rate. The resulting models are used by an optimization algorithm that calculates the optimal JSCC allocation for each video frame, and PHY transmission rate for each outgoing transport packet. The comprehensive simulation results obtained with the H.264/AVC codec demonstrate considerable increase in the PSNR of the decoded video when compared with a system that employs separately JSCC and PHY rate adaptation. Furthermore, our performance analysis indicates that the optimal PHY transmission rate calculated by the proposed algorithm, can be significantly different when compared with rate adaptation algorithms that target throughput improvement.     

Optimization network flow control and price coordination with feedback: proposal of a new distributed algorithm

The usage of price instruments was found to offer an interesting opportunity for reactive congestion flow control in communication or computer networks where the objective is to maximize the total utility of all traffic sources over their transmission rates. The proposed control mechanisms were, amongst others, based on the Price Method—known also as the Interaction Balance Method—and both synchronous and asynchronous versions were developed. It was, however, so far assumed that the transmission network model was known exactly and that no traffic was lost and the traffic routes were fixed and known to the transmitting sources. In this paper, we formulate the flow control problem in a modified form, allowing in particular for the routing decisions to be hidden from the sources, and recall the concept of the Price Coordination with Feedback (known also under the name of the Interaction Balance Method with Feedback—IBMF). It is then shown that the use of this approach allows for proposing the new distributed algorithm for pricing of network services. The existence of an optimal solution in steady state is proved under reasonable assumptions and the convergence issues are discussed. Two examples are provided to illustrate the operation of the proposed algorithm and to compare its performance with the usage of classical price coordination.     

基于拟牛顿方法的异步拥塞控制算法

针对当前网络中的拥塞问题,通过最优化问题引出网络效用最大化框架,在满足链路容量的约束下,最大化关于用户速率的效用函数,从而达到用户的最大满意程度。提出一种基于对偶原理的分布式速率异步控制算法,在链路算法设计过程中,用拟牛顿算法代替梯度算法,并且允许反馈延时时变。仿真结果表明,该算法收敛速度更快且满足公平性。     

移动通信延迟对信息并行传输效率影响仿真

通信信息并行传输效率是衡量移动通信网络系统性能的重要指标。为了提高移动网络信息通信速率,研究通信延迟对信息并行传输的影响。根据移动网络通信理论和IEEE802.11协议对移动网络信息并行通信过程进行分析,构建移动通信网络模型,得到串行延时、"数据包"的串行延时、路由延时、传播延时等参数,获取移动通信延迟与信息并行传输效率之间的约束关系,分析各个延迟参数对信息并行通信速率的影响。仿真结果表明,随着通信延迟的不断变化,信息并行传输速率也在发生变化,因此降低通信延迟能最大程度地提高信息并行传输速率。     

A game theoretic approach for power allocation with QoS constraints in wireless multimedia sensor networks

The distinctive features of wireless multimedia sensor networks (WMSNs) include application-specific quality-of-service (QoS) requirements and limited energy supply, with which each node makes its own decisions selfishly. Therefore this paper presents a power control game theoretic approach for WMSNs by studying the effect of transmission power on QoS and energy efficiency. The game approach determines the transmission strategy using utility optimization according to the fluctuation of channel states. Here, the utility function is defined by effective throughput per unit power while satisfying the user’s delay QoS constraints. The existence and uniqueness of Nash equilibrium for the proposed game are proved. Finally, the simulation results show that each user chooses the optimal transmission power to maximize its utility based on other constant parameters and the effects of delay constraints on the user’s utility are quantified as well.     

Quality enhancement for scalable video delivery over wireless networks based on particle swarm optimization

In this paper, the main objective is to find an optimal rate allocation strategy that can maximize the total-weighted quality of experience (QoE) associated with multiple video sources over error-prone multi-hop wireless networks based on the particle swarm optimization (PSO) technique which belongs to the family of swarm intelligence algorithms. In video transmission over such wireless networks, many network-based (packet loss, delay etc.) and source-based (encoding quantization level etc.) parameters can impair the perceived video quality. The main contributions of the proposed work are twofold. At first, an optimal bandwidth allocation framework is being developed based on PSO in which by incorporating an accurate video quality metric, the total weighted quality of experience of some competing video sources is being optimized. Second, these optimal rates have been used for differentiated QoE enforcement between multiple competing scalable video sources. The resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of a moderate scalable video encoder such as H.264/MPEG4 AVC. The aforementioned weight parameters are selected based on the importance of each video sequence’s quality and can be associated with some previous service level agreement-based prices. A strong motivation for differentiated quality enforcement is that video sources need to be encoded differently for different resolutions to cater to diverse devices from mobile displays to HDTV displays. Some numerical analysis have been presented to validate the theoretical results and to verify the claims.     

A combined neurodynamic approach to optimize the real-time price-based demand response management problem using mixed zero-one programming

This paper presents a microgrid system model considering three types of load and the user’s satisfaction function. The objective function with mixed zero-one programming is used to maximize every user’s profit and satisfaction in the way of the demand response management under real-time price. An energy function is used to transform the constrained problem into an unconstrained problem, and two neural networks are used to find the local optimal solutions of the objective function with different rates of convergence. A neurodynamic approach is used to combine the neural networks with the particle swarm optimization to find the global optimal solution of the objective function. The simulation results show that the combined approach is effective in solving the optimal problem.

     

无线传感网络中基于自动学习机的数据传输策略

尽管多径路由能够提高数据包传输成功率,但是其增加参与数据传输的节点数,提升了网络能耗。为此,提出基于自动学习机的单径路由(Single Path-based Learning Automation Routing,SPLAR)。设计SPLAR路由的主旨在于以最少能耗实现数据包传递率的最大化和端到端传输时延的最小化。通过自动学习机构建每个节点的动作集,并依据环境学习更新每个动作的概率。再选择具有最高概率的动作传输数据。仿真结果表明,提出的SPLAR路由在数据包传递率和端到端传输时延具有较好的性能。     

Neural network prediction model for a real-time data transmission

Both the real-time transmission and the amount of valid transmitted data are important factors in real-time multimedia transmission through the Internet. They are mainly affected by the channel bandwidth, delay time, and packet loss. In this paper, we propose a predictive rate control system for data transmission, which is designed to improve the number of valid transmitted packets for the transmission of real-time multimedia over the Internet. The one-step-ahead round-trip delay time and packet loss are predicted using a prediction algorithm and then these predicted values are used to determine the transmission rate. A real-time multimedia transmission system was implemented using a TCP-friendly algorithm, in order to obtain the measurement data needed for the proposed system. Neural network modeling was performed using the collected data, which consisted of the round-trip time (RTT) delay and packet loss rate (PLR). In addition, the performance of the neural network prediction model was verified through a validation process. The transmission rate was determined from the values of RTT delay and PLR, and a data transmission test for an actual system was performed using this transmission rate. The experiment results show that the algorithm proposed in this study increases the number of valid packets compared with the TCP-friendly algorithm.     

Application-layer multipath data transfer via TCP: Schemes and performance tradeoffs

For applications involving data transmission from multiple sources, an important problem is: when sources are allowed to use multiple paths, how does one select paths and control the sending rates on the paths to maximize the aggregate sending rate of the sources? We consider this problem in the context of an overlay network by allowing a source to send data over overlay paths to its destination. This problem is NP-hard, and we develop an iterative distributed heuristic to solve it. In each iteration, we first select paths and then control the sending rates on the multiple paths to maximize the aggregate sending rate of the sources. For rate control, we develop an application-level multipath rate controller via TCP. This controller is easy to deploy and maximizes the aggregate sending rate of the sources in certain settings. To the best of our knowledge, this is the first distributed application-level controller with such an optimality property. For path selection, we prove that the problem of optimal overlay path selection is NP-hard and propose randomized path-selection algorithms. Our performance evaluation demonstrates that our iterative heuristic performs very well in a wide range of settings. Furthermore, a small number of paths, 2–4, and a small amount of extra bandwidth in the network are sufficient to realize most of the performance gains.