A fuzzy control scheme for video transmission in Bluetooth wireless

This paper is concerned with transmission of Moving Picture Expert Group (MPEG) video over a Bluetooth wireless network using a fuzzy approach. MPEG Variable Bit Rate (VBR) video sources suffer from long delay and excessive loss due to the sudden bursts in bit rate. Constant Bit Rate (CBR) encoding scheme may work well for a network with a guaranteed bandwidth. However, a Bluetooth channel is subject to wireless interference and can never guarantee a constant bandwidth. Subsequently, it is impossible to transmit a CBR video over Bluetooth wireless without data loss or image quality degradation. To resolve this problem, a fuzzy control system is introduced at the Host Controller Interface (HCI). The system consists of a traffic-shaping buffer whose role is to prevent excessive back-to-back cells being generated during the peak transmissions of MPEG video sources. The output bit rate of the traffic-shaping buffer is controlled by a fuzzy controller to ensure that the video stream from the host conforms to the traffic condition of the Bluetooth channel. Another fuzzy controller regulates the average arrival bit rate to the traffic-shaper to guarantee that the buffer is neither full nor empty. Computer simulation results demonstrate that the use of the fuzzy controllers reduces excessive data loss at the HCI as compared with an open loop VBR/CBR video transmission in Bluetooth.     

An adaptive control for video transmission over bluetooth

This paper is concerned with transmission of a moving picture expert group (MPEG) video stream over a Bluetooth channel, using an adaptive neuro-fuzzy technique. MPEG variable bit rate (VBR) video sources over a network generally experience long delay and unacceptable data loss, due to high variations in bit rate. Furthermore, transmission rate could be unpredictable in a Bluetooth network due to interferences by other wireless devices or general Bluetooth channel noises. Subsequently, it is almost impossible to transmit VBR data sources over Bluetooth without excessive delay or data loss. In this work, an adaptive scheme is introduced so that the controller may adjust itself to the current state of the system under control. This paper utilizes a traffic-shaping buffer to prevent excessive back-to-back transmissions of MPEG VBR data sources. A novel adaptive neuro-fuzzy scheme regulates the output rate of the buffer to ensure that the video stream from the host conforms to the traffic conditions of the Bluetooth channel during the transmission period. The computer simulation results show that the use of the neuro-fuzzy controller reduces excessive delay and data loss at the host-controller-interface, as compared with a conventional VBR video transmission and a rule-based fuzzy controller (RBF1) in Bluetooth.     

Fuzzy-based rate control for real-time MPEG video

We propose a fuzzy logic-based control scheme for real-time motion picture expert group (MPEG) video to avoid long delay or excessive loss at the user-network interface (UNI) in an asynchronous transfer mode (ATM) network. The system consists of a shaper whose role is to smooth the MPEG output traffic to reduce the burstiness of the video stream. The input and output rates of the shaper buffer are controlled by two fuzzy logic-based controllers. To avoid a long delay at the shaper, the first controller aims to tune the output rate of the shaper in the video frame time scale based on the number of available transmission credits at the UNI and the occupancy of the shaper's buffer. Based on the average occupancy of the shaper's buffer and its variance, the second controller tunes the input rate to the shaper over a much larger time scale by applying a closed-loop MPEG encoding scheme. With this approach, the traffic enters the network at an almost constant bit rate (with a very small variation) allowing simple network management functions such as admission control and bandwidth allocation, while guaranteeing a relatively constant video quality since the encoding rate is changed only in critical periods when the shaper buffer “threatens” to overflow. The performance of the proposed scheme is evaluated through numerical tests on real video sequences     

基于模糊控制的ATM网络VBR视频传输平滑策略

VBR视频传输的突发性是影响ATM网络服务质量的关键因素,文中通过模糊控制方法对视频传输流量的阈值进行监控,实现了接入的平滑并可动态调整传输速度。文中以传输流量及这发级作为评价指标进行分析,结果表明VBR视频传输自适应平滑策略的实效性。     

Connection admission control of ATM network using integrated MLP and fuzzy controllers

This paper presents a new approach to the problem of call admission control (CAC) of variable bit rate (VBR) traffic in an asynchronous transfer mode (ATM) network. Our approach employs an integrated neural network and fuzzy controller to implement the CAC controller. This scheme capitalizes on the learning ability of a neural network and the robustness of a fuzzy controller. Experiments show that this scheme is able to achieve high throughput and low cell loss while achieving fairness among different classes of VBR traffic. For comparison, we have also implemented four other CAC schemes: (1) peak bandwidth method, (2) equivalent bandwidth method, (3) average bandwidth method and (4) neural network quality of service (QoS) predictor. Results of these experiments are presented in this paper.     

Joint rate control for VBR MPEG video on PVC ATM links

A joint rate control algorithm for variable bit rate (VBR) MPEG-compressed digital video on point-to-point permanent virtual circuit (PVC) ATM links is proposed. The algorithm controls the encoding mode of a number of video encoders that operate either on VBR or constant bit rate (CBR) mode. The algorithm selects the encoding mode based on the buffer occupancy of a multiplexer co-located with the encoders that interfaces them to the PVC link. VBR encoding is the predominant encoding mode used during congestion-free periods. CBR mode is used only during congested periods. When CBR is selected, new bit rates are jointly assigned to the encoders based on their relative encoding complexities. The bit rate assigned to the encoders are enforced by a CBR rate control local to each encoder. The performance of the joint rate control algorithm is evaluated through simulation of a packet multiplexer, where a number of connections are multiplexed onto a fixed-capacity channel. The performance is compared to that of multiplexing conventional CBR and open-loop VBR from the video quality and bandwidth efficiency points of view. Simulation results show that the proposed algorithm improves performance over multiplexed conventional CBR or open-loop VBR MPEG video, without significantly increasing implementation complexity. The application of the algorithm to video-on-demand over ATM is discussed.     

Neuro-Fuzzy Modeling and Prediction of VBR MPEG Video Sources

The work presented in this paper intends to apply neuro-fuzzy methods for the modeling and prediction on traffic intensity of digital video sources which are coded with hybrid Motion Compensation/Differential Pulse Code Modulation/Discrete Cosine Transform (MC/DPCM/DCT) algorithm. Although current coding standards recommend constant bit rate (CBR) output by means of a smoothing buffer, the hybrid algorithm inherently produces variable bit rate (VBR) output. This paper describes the novel application of a fuzzy predictor for the purposes of modeling and prediction on video sources. The computation requirement of the fuzzy predictor and its neural network implementation are also discussed. The proposed fuzzy prediction method and its neural network version can be applied to the development of connection admission control, usage parameter control and congestion control algorithms in ATM networks.     

VBR MPEG视频源流量的智能集成预测模型

由于VBR视频流量的预测能力是直接关系缓冲区设计、动态带宽分配及拥塞控制等提高网络服务质量的关键因素,因此,以多媒体主要应用形式VBRMPEG视频源为研究对象,提出了一种基于模糊神经网络的视频流量智能预测模型。通过设计模糊预测器减少输出比特流的预测误差,采用神经网络减少多步预测的计算量。仿真试验表明,与标准AR模型预测结果相比,该模型显著提高了预测的准确度和可靠性,且易于推广使用。     

MPEG VBR video traffic modeling and classification using fuzzytechnique

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters     

Traffic characterization algorithms for VBR video in multimedia networks

A network that offers deterministic, i.e., worst case, quality-of-service guarantees to variable-bit-rate (VBR) video must provide a resource reservation mechanism that allocates bandwidth, buffer space, and other resources for each video stream. Such a resource reservation scheme must be carefully designed, otherwise network resources are wasted. A key component for the design of a resource reservation scheme is the traffic characterization method that specifies the traffic arrivals on a video stream. The traffic characterization should accurately describe the actual arrivals, so that a large number of streams can be supported; but it must also map directly into efficient traffic-policing mechanisms that monitor arrivals on each stream. In this study, we present a fast and accurate traffic characterization method for stored VBR video in networks with a deterministic service. We use this approximation to obtain a traffic characterization that can be efficiently policed by a small number of leaky buckets. We present a case study where we apply our characterization method to networks that employ a dynamic resource reservation scheme with renegotiation. We use traces from a set of 25–30-min MPEG sequences to evaluate our method against other characterization schemes from the literature.     

Adaptive bandwidth allocation based on playback tunnel to support video on demand service over ATM networks

This paper focuses on adaptive bandwidth allocation (ABA) for transporting pre-stored VBR compressed video data across networks in support of video-on-demand (VoD) service. Two key issues are addressed. Firstly, a simplified dynamic programming method based on playback tunnel is proposed to predict the bandwidth which can be dynamically allocated for transporting video segment to guarantee that the playback buffer neither underflows nor overflows. Secondly, ABA schemes with different criteria including middle-tunnel ABA (MT-ABA) and reduced-bandwidth-variability ABA (RBV-ABA) are investigated. The proposed ABA schemes are evaluated by a set of real-life MPEG video traces. The obtained results show that the RBV-ABA scheme outperforms MT-ABA scheme in terms of peak rate, traffic burstiness and bandwidth variability. On the other hand, the MT-ABA scheme is more significant for avoiding the playback buffer overflow or underflow than that of RBV-ABA.     

Application of neural-fuzzy controller for streaming video over Bluetooth 1.2

This paper is to introduce an application of Computational Intelligence (CI) to Moving Picture Expert Group-4 (MPEG-4) video compression over IEEE.802.15.1 wireless communication, known as Bluetooth 1.2, in order to improve picture quality. The 2.4 GHz Industrial, Scientific and Medical frequency band is used for the IEEE.802.15.1 standard. IEEE.802.15.1 can be affected by noise and interference due to other neighboring wireless devices sharing the same frequency carrier. The noise and interference create difficulties in ascertaining an accurate real-time transmission rate at the receiving end. Furthermore, the MPEG-4 codec is an object-oriented compression system and demands a high bandwidth. It is therefore difficult to avoid excessive delay, image quality degradation and/or data loss during MPEG-4 video transmission over standard systems. A new buffer entitled ‘buffer added’ has been introduced at the input of the Bluetooth 1.2 device. This buffer is controlled by a Rule-Based Fuzzy (RBF) logic controller at the input and a neural-fuzzy controller (NFC) at the output. The two new fuzzy rules manipulate and supervise the flow of video over the Bluetooth 1.2 standard. The computer simulation results illustrate the comparison between a non-CI video transmission over Bluetooth 1.2 and the proposed design, confirming that the applications of RBF and NFC do improve the image quality, reduce data loss and reduce time delay.     

一种基于模糊神经网络的VBR MPEG视频流量预测模型

针对VBR MPEG视频流的复杂特性，充分利用人工智能方法的优势，提出了一种基于模糊神经网络的视频流量预测模型，利用模糊逻辑模型达到减少预测误差的目的，采用神经网络满足网络通信的实时性要求．实验结果表明，该模型比传统AR模型显著提高了预测的准确度和可靠性．     

视频编码中考虑模式选择的位率优化控制

本文在传统位率控制方法的基础上提出一种新算法,即基于宏块的最佳位率控制算法．主要思想是在考虑还宽,比特率和缓冲区大小的同时,考虑不同宏块模式的选择对位率的影响,文中给出了一些相关结果并将其与传统方法所得数据相比较,结果表明在多数位率条件下用本算法得到的ＰＳＮＲ比传统方法要好．     

基于MPEG视频流的动态带宽分配算法

ATM是兴起的宽带网标准,基于报文的视频正是宽带网上的一个典型应用.变位率视频的传输方法为恒定的视频质量提供了可能,但是在传输过程中,报文的丢失、延迟和延迟抖动等因素都会影响视频质量,因此有必要提供QoS保证.提出了两种新的在ATM网上进行MPEG视频传输的动态带宽分配算法,一种是动态计算算法,另一种是动态虚拟缓存算法.在这些算法中,根据允许的丢包率,动态地调整MPEG视频带宽以保证QoS.     

An Admission Control Scheme Based on Online Measurement for VBR Video Streams Over Wireless Home Networks

This paper presents an online measurement-based admission control scheme on the basis that the aggregate VBR video traffic is lognormally distributed. The proposed scheme consists of two components: measurement process and admission decision. The measurement process applies a linear Kalman filter to estimate statistical parameters of aggregate VBR video traffic. The estimated statistical parameters are used to calculate the effective bandwidth for admission decision. Variable bit rate (VBR) video traffic with high data rate is expected to occupy a dominant proportion of bandwidth for future wireless broadband home networks. To guarantee quality-of-service (QoS) of such VBR video streams, while achieving a high level of channel utilization, an efficient admission control scheme is urgently required, especially for emerging wireless multimedia indoor services, such as HDTV, online video game, etc. The proposed scheme is computationally efficient and accurate without much prior traffic information. Simulation results verify its effectiveness and show that it performs well for both a small number of connections and a large number of connections.     

基于混合预测模型的VBR流拥塞控制机制

针对VBR多媒体流量的数字特征,在混合网络流量预测模型的基础上,结合小波分析和卡尔曼滤波,提出一种多媒体流自适应带宽分配算法,以合理分配网络带宽,利用双重漏桶控制网络流量。在NS网络仿真平台上进行的仿真实验表明,该算法能有效避免网络拥塞,降低网络传输时延和数据丢包率,提高带宽利用率。     

面向RCBR网络的变比特率MPEG视频流的自适应预测

变比特率视频业务是将来多媒体业务的主要组成部分，为保证高质量，实时传输的需要，准确把握视频特征，结合网络传输机制为多媒体通信提供更好的服务质量（QoS)是目前亟待解决的问题之一，该文针对面向RCBR（Renegotiated Constant Bit Rate)网络的变比特率MPEG视频提出了基于编码结构的自适应预测算法，它充分利用了视频流的周期相关性，能准确快速，无延迟地对视频帧的码率进行预测，通过网络动态地为视频分配合适的带宽，不仅能达到高质量，低延迟抖动的视频服务，并且提高了网络的利用率，与分配固定带宽的网络相比，其网络缓冲的排除性能和网络的利用率有明显提高。     

基于主动抛弃的MPEG—2 TS流速率控制算法

在网络传输能力不足以实时传输MPEG-2码流时，接收端的图象恢复质量会因为在网络传送中不可避免的、随机的数据丢失而严重下降，为了解决这一问题，提出了一种基于主动抛弃的MPEG-2 TS流速率控制算法，即在原始码流输送到网络前，根据预测的网络传输阻塞状况，预先主动抛弃部分不太重要的数据，从而使实际输出码率与可用网络带宽匹配，以保护重要数据和改善接收端的图象恢复质量，并提高网络带宽的利用率，经实验测试证明，此算法简单、实用、有效、且接收端的图象恢复质量比任凭网络随机丢失的情况有明显改善。     

Real-time applications in a CRMA network: a performance analysis

This paper analyses bandwidth allocation schemes for managing real-time applications (Variable Bit Rate video and voice) in a CRMA network. A methodology to compute the Quality of Service (QoS) experienced by variable bit rate (VBR) video and voice sources is proposed. As VBR video applications only tolerate extremely low packet loss rates (< 10⁻⁸), we need a computational approach to estimate very low tail probabilities. Studying the QoS with a simulation technique is not feasible, because computational costs make it almost impossible to estimate tail distribution probabilities lower than 10⁻²−10⁻³. Therefore, to achieve this target, we propose a model which represents a CRMA network's worst case behaviour (i.e. a scenario with maximum network congestion), and which can be solved analytically. By solving this model for different bandwidth allocation schemes, we can obtain the corresponding bounds on the QoS experienced by VBR video users. Finally, for those bandwidth allocation schemes which provide an acceptable QoS for VBR video traffic, we also estimate (via a trace-driven simulation) the QoS achieved by voice users.