Predicting the quality of video transmission over best effort network service

Author(s):  
W. Pattara-Atikom ◽  
S. Banerjee ◽  
P. Krishnamurthy
2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng

During video transmission over error prone network, compressed video bit-stream is sensitive to channel errors that may degrade the decoded pictures severely. In order to solve this problem, error concealment technique is a useful post-processing tool for recovering the lost information. In these methods, how to estimate the lost motion vector correctly is important for the quality of decoded picture. In order to recover the lost motion vector, an Decoder Motion Vector Estimation (DMVE) criterion was proposed and have well effect for recover the lost blocks. In this paper, we propose an improved error concealment method based on DMVE, which exploits the accurate motion vector by using redundant motion vector information. The experimental results with an H.264 codec show that our method improves both subjective and objective decoder reconstructed video quality, especially for sequences of drastic motion.


Author(s):  
M. Amreev ◽  
R. Safin ◽  
T. Pavlova ◽  
E. Temyrkanova ◽  
Y. Garmashova

The use of video surveillance systems is used in the areas of security, law and order, in the territories of protected objects, in monitoring the movement of road vehicles and in other areas. The main disadvantage of a video surveillance system is its susceptibility to weather influences (rain, fog, snowfall, etc.), which degrades the quality of the video system by reducing the signal level. Therefore, the urgency of finding new ways and possibilities to improve the quality of video signals is one of the priority areas of signal processing. The main task of this work was to determine the main parameters, simulate the transmission line and amplifier, and select the schematic diagram of the transmitting and receiving path with the voltage and current ratings. Both the receiver and the cable video transmitter have different means of adjusting to different transmission line lengths. The signal at the output of each receiver should be in the range from 0.9 to 1.1 V, and the spread of the total ohmic resistance of the wires of the video transmission line at the input of the receiver should be no more than 2 – 3%. Based on these parameters, the equipment is configured for transmitting video over the channel. The magnitude of the mismatch is regulated by potentiometers, which allow smooth adjustment of the video transmission equipment [1]. As a rule, video transmission over the channel is carried out at a distance of 50 to 1500 m. If it is necessary to transmit video at distances less than 50 m, additional resistances are connected in series at the receiver input so that the total line resistance is 30 - 50 Ohm [1].


Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.


Author(s):  
André F. Marquet ◽  
Jânio M. Monteiro ◽  
Nuno J. Martins ◽  
Mario S. Nunes

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.


2002 ◽  
pp. 106-122
Author(s):  
Luiz A. DaSilva

Today’s networks support applications that deliver text, audio, images and video, often in real time and with a high degree of interactivity, using a common infrastructure. More often than not, traffic is carried over packet-switched networks that treat all data the same, under what is known as best-effort service. Packet switching can achieve very high efficiency through statistical multiplexing of data from numerous sources; however, due to the very nature of packet switching, one should expect fluctuations in throughput, delay, reliability, etc., for any given flow. The greater the statistical multiplexing capabilities, the greater the efficiency and also the greater the variability of achieved performance; in this sense, best-effort service provides maximum efficiency with highly unpredictable service quality. Clearly, not all traffic flows are created equal. Interactive web-based applications tend to be very sensitive to throughput, while real-time voice and video are sensitive to delay and jitter, and traditional data applications such as e-mail and file transfers are fairly insensitive to fluctuations in performance. The concept of quality of service (QoS) has evolved from the realization that in networks that carry heterogeneous traffic it makes sense to treat specific classes of traffic according to their specific needs.


2005 ◽  
Vol 1 (1) ◽  
pp. 9 ◽  
Author(s):  
Ronan De Renesse ◽  
Vasilis Friderikos ◽  
Hamid Aghvami

Ad hoc and sensor networks have received tremendous attention in the recent literature due to its unpredictable nature and its many applications. Imposing any kind of reliability in such networks represents a real challenge. In this paper, we propose a new resource management scheme which virtually reserves and releases resources at the network layer when necessary. Results show that our scheme distributes resources efficiently between Best Effort and Quality of Service traffics even when congestion arises.


2012 ◽  
Vol 241-244 ◽  
pp. 2354-2361
Author(s):  
Ling Song ◽  
Tao Shen Li ◽  
Yan Chen

Real-time video transmission demands tremendous bandwidth, throughput and strict delay. For transmitting real-time video in the multi-interface multi-channel Ad hoc, firstly, we applied multi-interface multi-channel extension methods to the AOMDV (Ad-hoc On-demand Multipath Distance Vector) routing protocol, and improved extant channel switching algorithm, called MIMC-AOMDV (Multi-Interface Multi-Channel AOMDV) routing protocol. Secondly, we proposed video streaming delay QoS(Quality of Service) constraint and link-quality metrics, which used the multi interface queue’s total used length to get QMMIMC-AOMDV (Quality metric MIMC -AOMDV) routing protocol. The simulations show that the proposed QMMIMC-AOMDV can reduce the frame delay effectively and raise frame decodable rate and peak signal to noise ratio (PSNR), it is more suitable for real-time video streams.


Author(s):  
Samir Kumar Sadhukhan ◽  
Swarup Mandal

It is an established fact that cost of churning is a common concern for being profitable in the cellular network service provider’s space. Service providers can view this problem as a service management problem and can have a solution to enhance the stickiness of subscribers by managing the quality of user experience. Quality of Experience (QoE) is important in contrast to Quality of Service (QoS). Three basic components of service management are stage, prop, and user experience. In this cellular network service context, network infrastructure acts as prop. Prop needs to be flexible to enable the personalization in providing the service. In reality the major challenge for a service provider is keep the fitment between prop and the dynamic changes in subscriber profile in a cost effective manner. To define the problem more precisely, the authors take the conventional UMTS cellular network. Here, operators have considered single-homing of RNCs to MSCs/SGSNs (i.e., many-to-one mapping) with an objective to generate service at lower cost over a fixed period of time. However, a single-homing network does not remain cost-effective and flexible anymore when subscribers later begin to show specific inter-MSC/SGSN mobility patterns over time. This necessitates post-deployment topological extension of the network in which some specific RNCs are connected to two MSCs/SGSNs via direct links resulting in a more complex many-to-two mapping structure in parts of the network. The authors formulate the scenario as a combinatorial optimization problem and solve the NP-Complete problem using three meta-heuristic techniques, namely Simulated Annealing (SA), Tabu search (TS), and Ant colony optimization (ACO). They then compare these techniques with a novel optimal heuristic search method that the authors propose typically to solve the problem. The comparative results reveal that the search-based method is more efficient than meta-heuristic techniques in finding optimal solutions quickly.


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