Identification of adaptive video streams based on traffic correlation

Due to the popularity of Dynamic Adaptive Streaming Over HTTP (DASH), broadband and Internet service providers’ links transmit mainly multimedia content. As the most popular providers encrypt their video services, the attempts to identify their traffic through Deep Packet Inspection (DPI) encounter difficulties. Therefore, encrypted DASH traffic requires new classification methods. In this work, we propose to identify DASH traffic taking into account statistical dependencies among video flows. For this purpose, we employ cluster analysis which can identify groups of traffic flows that show similarity using only the application level information. In our work, we applied three unsupervised clustering algorithms, namely MinMax K-Means, OPTICS and AutoClass, to classify video traces obtained from an emulated environment. The experimental results show that the employed algorithms are able to effectively distinguish video flows generated by different play-out strategies. The classification performance depends on the network conditions and parameters of the learning process.

     

Sparse basis selection: new results and application to adaptive prediction of video source traffic

Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.     

Dynamic pricing with traffic engineering for adaptive video streaming over software-defined content delivery networking

Multimedia Tools and Applications - Multimedia content has become widespread in network traffic. The high volume of data and flexibility must be addressed to guarantee the quality of experience...     

Performance analysis of adaptive buffer management mechanism using autoregressive based MPEG-4 encoded video traffic model

The burstiness of video traffic in future wired and wireless networks makes dynamic management of network resources more critical. This helps to meet stringent delay and loss requirements of video traffic and improves its Quality of Service (QoS). Since buffer management in queueing system plays an important role towards effective control of QoS for various types of applications, we have focused on its dynamic management. In this paper, we have proposed ADPBS scheme for priority queueing system. The performance of this scheme is analyzed with MPEG-4 encoded video sequence as input to the ADPBS queue. The simulation is carried out in MATLAB for various combinations of threshold control parameters, different traffic load and input traffic mix variations. The results of simulations indicate that dynamically controlled threshold in ADPBS contributes to significant reduction of packet loss of different priority classes as compared with static partial buffer sharing queue and first in first out queue based on threshold control parameters and the traffic type.     

An adaptive type-2 fuzzy traffic engineering method for video surveillance systems over software defined networks

Multimedia Tools and Applications - Software Defined Network (SDN) is a new network technology which allows network providers to afford predefined Quality of Service (QoS) for video streaming...     

EPCRTT-based smoothing and multiplexing of VBR video traffic

Applying video smoothing techniques to real-time video transmission can significantly reduce the peak rate and rate variability of compressed video streams. Moreover, statistical multiplexing of the smoothed traffic can substantially improve network utilization. In this paper we propose a new smoothing scheme, which exploits statistical multiplexing gain that can be obtained after smoothing of individual video streams. We present a new bandwidth allocation algorithm that allows for responsive interactivity. The local re-smoothing algorithm is carried out using an iterative process. In the proposed scheme the smoothed video streams are divided into fixed intervals and then a new transmission schedule for each interval is calculated. The problem of applying an optimal transmission schedule for aggregated smoothing video streams is shown to be NP-hard problem. Partitioning the whole stream into sections enables parallel processing of the smoothing algorithm in real-time before transmission. This approach allows partial transmission of the multiplexed stream while smoothing other intervals. The simulation results show a significant reduction in peak rate and rate variability of the aggregated stream, compared to the non-smoothing case. Therefore the proposed scheme allows us to increase the number of simultanusally-served video streams.

视频交通车辆信息检测器的设计与实现

针对近年来城市交通的拥挤现象,特别是驾驶员违章严重、交通事故频发等问题,介绍了在现代交通控制和管理系统中占有十分重要地位的传感器——视频车辆检测器。主要利用CCD摄像机与图像处理技术,完成了视频交通车辆信息检测器,给出了系统的软件和硬件构架,并详细分析了关键计算手段与方法。     

Measuring and modelling sewer pipes from video

This article presents a system for the automatic measurement and modelling of sewer pipes. The system recovers the interior shape of a sewer pipe from a video sequence which is acquired by a fish-eye lens camera moving inside the pipe. The approach is based on tracking interest points across successive video frames and posing the general structure-from-motion problem. It is shown that the tracked points can be reliably reconstructed despite the forward motion of the camera. This is achieved by utilizing a fish-eye lens with a wide field of view. The standard techniques for robust estimation of the two- and three-view geometry are modified so that they can be used for calibrated fish-eye lens cameras with a field of view less than 180°. The tubular arrangement of the reconstructed points allows pipe shape estimation by surface fitting. Hence, a method for modelling such surfaces with a locally cylindrical model is proposed. The system is demonstrated with a real sewer video and an error analysis for the recovered structure is presented.     

基于视频流量特性分析的流量控制机制研究

流量控制是保证服务质量、提高网络资源利用率的有效手段,接纳控制作为流量控制的有效途径之一,与网络的流量特征、网络性能有密切关系,尤其在系统重负载时比拥塞控制更为有效.在基于跟踪流量分析视频流量特性的基础上,研究了基于流量模型的流量控制机制的部署与实现,探讨了基于复杂流量模型进行有效带宽估计的接纳控制机制是否能够提高系统性能和网络的复用性能,并通过仿真对典型机制进行了性能比较,最后给出了今后流量复用研究的建议.     

Efficient compression and network adaptive video coding for distributed video surveillance

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.     

Distributed and adaptive traffic signal control within a realistic traffic simulation

As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.     

Ground truth annotation of traffic video data

This paper presents a software application to generate ground-truth data on video files from traffic surveillance cameras used for Intelligent Transportation Systems (IT systems). The computer vision system to be evaluated counts the number of vehicles that cross a line per time unit –intensity-, the average speed and the occupancy. The main goal of the visual interface presented in this paper is to be easy to use without the requirement of any specific hardware. It is based on a standard laptop or desktop computer and a Jog shuttle wheel. The setup is efficient and comfortable because one hand of the annotating person is almost all the time on the space key of the keyboard while the other hand is on the jog shuttle wheel. The mean time required to annotate a video file ranges from 1 to 5 times its duration (per lane) depending on the content. Compared to general purpose annotation tool a time factor gain of about 7 times is achieved.     

Classification of slice-based VBR video traffic and estimation of link loss by exceedance

Classification of a video stream is an essential preliminary step to estimate the bit loss when the video stream is transmitted over a communication network. In this paper, we classify the video frames by the average frame size and estimate the bit loss for each class when the bitrate exceeds the capacity of the bottleneck link. The video stream under study is encoded using the explicit slice-based H.264/AVC encoding scheme. This scheme reduces the burstiness of regular H.264/AVC encoded video by removing the traditional GOP structure. Instead, a repetitive combination of intracoded and predicted slices is employed, thereby introducing a specific dependence structure in the video data. We consider a bufferless model of the communication system and evaluate the channel capacity required to give a maximum allowed loss rate for each class.Due to the high variability, non-stationarity and non-homogeneity of the underlying video data, the obtained classes are checked regarding the dependence and distribution structure of the data. The high quantiles of the losses are estimated for each class.     

交通流视频检测中车辆的检测与跟踪

交通流视频检测技术是实现城市交通现代化管理的核心技术之一,也是智能交通系统的重要组成部分.使用改进混合高斯分布模型来表征图像帧中每一个像素点的亮度特征,使用绝对值差分提取出运动车辆的轮廓,采集目标的轮廓特征等数学特征,采用相似性算子跟踪多运动目标,采用面积阚值排除非车因素的干扰.最后在城市交通流实况检测中验证算法.     

Traffic and video quality with adaptive neural compression

Video sequences are major sources of traffic for broadband ISDN networks, and video compression is fundamental to the efficient use of such networks. We present a novel neural method to achieve real-time adaptive compression of video. This tends to maintain a target quality of the decompressed image specified by the user. The method uses a set of compression/decompression neural networks of different levels of compression, as well as a simple motion-detection procedure. We describe the method and present experimental data concerning its performance and traffic characteristics with real video sequences. The impact of this compression method on ATM-cell traffic is also investigated and measurement data are provided.     

Generation of synthetic video traffic using time series

Generating traffic has always been an important part of network simulations but has turned to an even more challenging task with modern networks. The statistical properties of the input stochastic processes traced in the networks used all along Information Era turned out to be complicated and difficult to reproduce. Taking into account successful efforts in modeling Internet traffic with FARIMA time series models, this paper attempts to extend their applicability and employ them to generate synthetic video traffic. It is known that FARIMA can model both the Short Range (SRD) and Long Range Dependence (LRD) existing in video traffic; however the traces it produces fail to describe correctly the moments (mean, standard deviation, skewness, kurtosis) of the distribution behind the data. Since an efficient traffic generator should capture both the statistical properties and queuing behavior of video traffic we experiment with models such as FARIMA with Student's t errors and FARIMA-GARCH with Normal and Student's t errors, improving somewhat the accuracy of the generated traffic. Furthermore, the paper suggests the projection of the traces generated by a FARIMA model to values of a Lognormal distribution. It is shown that such a methodology produces synthetic traces that can emulate very closely the behavior of real traces. In order to quantify closeness the generated traces are fed into a simple FIFO queuing system with finite buffers, where loss probability is calculated and compared to that experienced by the corresponding real traces. Using five different real traces, MPEG-4 or H.263, it is shown that the proposed methodology produces traffic generators that can capture satisfactorily several statistical properties of the real traffic and also its queuing behavior for a wide range of buffer sizes and service rates.     

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     

On the trajectory of video quality transition in HTTP adaptive video streaming

HTTP adaptive video streaming matches video quality to the capacity of a changing context. A variety of schemes that rely on buffer state dynamics for video rate selection have been proposed. However, these schemes are predominantly based on heuristics, and appropriate models describing the relationship between video rate and buffer levels have not received sufficient attention. In this paper, we present a QoE-aware video rate evolution model based on buffer state changes. The scheme is evaluated within a real-world Internet environment. The results of an extensive evaluation show an improvement in the stability, average video rate and system utilisation, while at the same time a reduction in the start-up delay and convergence time is achieved by the modified players.