Elsevier

Computer Networks

Volume 57, Issue 7, 8 May 2013, Pages 1726-1738
Computer Networks

QoE management for video conferencing applications

https://doi.org/10.1016/j.comnet.2013.03.002Get rights and content

Abstract

We introduce a framework for managing the QoE of videos coded with the H.264 codec and transmitted by video conferencing applications through limited bandwidth networks. We focus our study on the medium-motion videos with QCIF, CIF, and VGA resolutions, the most pervasive video formats used by video conferencing applications across the Internet and cellular telephony systems. Using subjective tests for measuring the level of video quality perceived by end users, we expose the relation between the main influential video parameters and the quality experienced by end users. Furthermore, after investigating the effect of different frame rates and compression levels on video streaming bit rate, and consequently on QoE, we propose a QoE control mechanism for limited-bandwidth situations. A congestion control technique is also introduced in this paper and used in simulations for verifying the efficiency of the proposed QoE management algorithm and to implement this algorithm for practical applications.

Introduction

In recent years, the use of multimedia applications and consequently streaming of video data over the Internet has rapidly increased. Furthermore, to reduce storage space and to transmit video over bandwidth-limited networks, compression of video bitstream is essential. To compress video data, the H.264 codec [1]—the state of the art in video compression—employs, among other techniques, spatial transforms and motion compensated prediction between consecutive frames to exploit spatial and temporal redundancy, respectively. According to the dynamic nature of video content, the data rate of coded bit streams can be changed on the fly. Moreover, the “best effort” nature of the Internet makes it a competitive environment for different applications to increase their throughput; hence congestion and consequently loss and delay inevitably happen within the network. Despite the indisputable benefits of compression, the compressed video data is highly vulnerable to data loss. Indeed, dependency of each coded frame to previous frames’ data means that any error due to loss is propagated to subsequent frames. Thus, the distortion caused by data loss interferes with the objective of video quality. As we have moved to a unique (IP) network for multiple services, it has appeared that traditional network-level QoS parameters do not tell a sufficient story for media quality and the focus for quality assessment has moved to quality of experience (QoE) which has been defined by the ITU-T as the overall acceptability of an application or service, as perceived subjectively by the end-user. Further, since the video’s bit rate varies because of different video characteristics such as frame rate, resolution, compression level, and content, a similar network situation may cause end users to perceive a different level of quality for different videos.

Video conferencing is currently commonly employed over the Internet, and it is also expected that video chatting will be one of the key business areas for mobile service providers (e.g., 3G and 4G). To meet customer expectations, service providers should know the level of quality which is found acceptable by customers. Based on this information, service providers need to manage and control resources efficiently. However, managing and deploying more resources not only increases costs but also sometimes is not possible (e.g., in mobile environment, the bandwidth cannot be more than a certain level). Therefore, designing flexible (intelligent) applications, which can dynamically adapt themselves with existing networks by managing the video system (e.g., bit rate) without adverse effect on end-users’ perceived quality, has become an overwhelmingly important issue. In other words, QoE management by video conferencing applications is meant to lead to more efficient and economic deployment of available resources while keeping the end user’s satisfaction at an acceptable level. Control mechanisms for QoE include monitoring of the information regarding the network and end users’ condition as well as adjusting the corresponding influential factors. For video streaming, the Scalable Video Coding extension of codec H.264 (H.264/SVC) provides a solution for spatial, temporal, and quality scalability with a smooth switching between different bit rates streaming [2].

Two main questions this paper tries to answer are “what is the actual perceived video quality when video parameters are changed to meet the bandwidth limitation?” and “what are the best video parameters for specific video bit rates considering the perceived (subjective) quality by the end users?” This paper investigates the effect of different coded-video factors such as frame rate and quantization parameter (QP) on video data bit rate and perceived video quality. Further, it looks into QoE control through adjusting these video parameters given the bandwidth limitations imposed by the network. To focus our study and make new contributions to the extant literature, we have selected the QCIF (176 × 144), CIF (352 × 288), and VGA (640 × 480) video resolutions and medium motion video content (e.g., talking head) which are heavily used in video conferencing applications in the Internet and mobile networks.

This paper’s prominent contributions are threefold; (1) extensive measurement studies for investigating the effect of different control parameters (i.e., frame rate and quantization) on bit rates limited by network bandwidth have been conducted; (2) we present the results of subjective tests conducted for measuring the end-users’ perceived video quality, to find the optimum video parameters based on the given network bandwidth and acceptable QoE level; and (3) we propose a QoE control algorithm based on the mentioned measurements. To conduct simulations for verifying the efficiency of the proposed QoE management algorithm and to implement this algorithm in the practical applications, a congestion control technique derived from prior art is introduced in this paper. It should however be noted that the QoE control algorithm put forth in this paper is independent of the proposed congestion control technique and can be employed in combination with different congestion control algorithms.

The rest of the paper is organized as follows: Section 2 introduces the principle of QoE measurement as well as recent studies regarding the effect of video parameters on the QoE. Section 3 presents the coding results for different video parameters. The details of subjective QoE measurement tests and their outcomes are presented in Sections 4 and 5. In Section 6 our QoE control algorithm is proposed. Simulations and numeric results demonstrate the effectiveness of the proposed algorithm relative to others in Section 7. Section 8 concludes the paper and points to future work.

Section snippets

QoE Measurement

The level of quality of service provided by Internet networks can be assessed through gathering customer satisfaction data from end-users; therefore, QoE assessment means measuring the customer’s (end-user) satisfaction level. In other words, QoE is the overall performance of a system from the users’ point of view. QoE is a measure of end-to-end performance at the service level from the user’s perspective and an indication of how well the system meets the user’s needs [3].

Although QoE is

Effect of video parameters on video bit rate

We have investigated the effects of frame rate and quantization parameter (i.e., related to compression level) on the video bit rate. Since our research focus has been on video conferencing over the Internet, we have considered medium-motion (e.g., talking head) and QCIF (176 × 144), CIF (352 × 288), and VGA (640 × 480) as video content type and resolution formats, respectively. Standard Akiyo and Foreman videos with 10-s length were selected as the medium motion sequences (i.e., similar to video

Subjective assessment experimental setup

To examine the effects of the variation of frame rate and compression level of the video sequence on end-users’ perceived quality, we have conducted 96 subjective tests (32 video clips for each of QCIF, CIF and VGA resolutions discussed in Section 3).

Experimental results

The mean of the rating scores was calculated for 32 video clip tests (i.e., 8 different compression levels for 4 different frame rate videos) for each resolution. Fig. 3 presents the MOS of perceived video quality for different frame rates and compression levels. These MOSs with their associated 95% confidence intervals are also reported in Table 1.

Transport and QoE control

Since video conferencing applications are supposed to control the video parameters based on network condition, accurate monitoring and measurement of network parameters is vital. Using the bandwidth estimate or TCPF rate as an upper bound of the streaming bit rate, applications should choose the best video parameters to have the highest quality experienced by users under such conditions.

The control system exploiting measured network situation (e.g., available bandwidth) is sketched in Fig. 6.

Validation

To investigate the efficiency and applicability of the proposed (i) bandwidth estimation, (ii) congestion control algorithm, and (iii) QoE control algorithm, the network scenario shown in Fig. 8 is simulated using the NS-2 software [49].

To show the accuracy of the bandwidth estimation based on (6), node 1 streams the QCIF-size video data to node 2. The frame rate and streaming bit rate are set to 30 fps (30 packets per second) and 240 Kbps, respectively. Node 3 generates and sends a constant bit

Conclusion

In this paper, a framework for managing the QoE for video coded with H.264 over limited bandwidth networks has been introduced. Unlike other similar studies, we have specifically focused on medium-motion videos with QCIF, CIF, and VGA resolutions, the most pervasive video formats used by video conferencing applications across the Internet and mobile communication systems. The video streaming bit rate has been adjusted through changing the frame rates and compression levels to manage the QoE.

To

Ahmad Vakili received the B.Sc. and M.Sc. degrees in electrical engineering from Tehran Polytechnic in 1999 and 2002, respectively, and is currently working toward the Ph.D. degree in telecommunications at INRS-EMT, Montreal. His past experience include academic position with Azad University in Iran as a lecturer, as well as industrial position with A.S.A Consultant Company. He is currently a Research Assistant at the Energy, Materials, and Telecommunications Center of the National Institute of

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    Ahmad Vakili received the B.Sc. and M.Sc. degrees in electrical engineering from Tehran Polytechnic in 1999 and 2002, respectively, and is currently working toward the Ph.D. degree in telecommunications at INRS-EMT, Montreal. His past experience include academic position with Azad University in Iran as a lecturer, as well as industrial position with A.S.A Consultant Company. He is currently a Research Assistant at the Energy, Materials, and Telecommunications Center of the National Institute of Scientific Research, Canada. His research interests include multimedia communication services, QoS, and QoE management.

    Jean-Charles Grégoire has received the Bachelors degree in Electrical Engineering from the FacultÈ Polytechnique de Mons, Belgium, the Masters Degree in Mathematics from the University of Waterloo, Canada and the Ph.D. Degree from the Swiss Federal Polytechnic, Lausanne. His research interests cover all aspects of telecommunication systems engineering, including protocols, distributed systems, network design and performance analysis, and more recently security. He has also made contributions in the area of formal methods.

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