Volume 2008, Article ID 437128,15pages doi:10.1155/2008/437128
Research Article
A Transparent Loss Recovery Scheme Using Packet Redirection
for Wireless Video Transmissions
Chi-Huang Shih,1Ce-Kuen Shieh,1and Wen-Shyang Hwang2
1Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
2Department of Electrical Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan
Correspondence should be addressed to Chi-Huang Shih,[email protected]
Received 1 October 2007; Revised 14 February 2008; Accepted 17 March 2008 Recommended by F. Babich
With the wide deployment of wireless networks and the rapid integration of various emerging networking technologies nowadays, Internet video applications must be updated on a sufficiently timely basis to support high end-to-end quality of service (QoS) levels over heterogeneous infrastructures. However, updating the legacy applications to provide QoS support is both complex and expensive since the video applications must communicate with underlying architectures when carrying out QoS provisioning, and furthermore, should be both aware of and adaptive to variations in the network conditions. Accordingly, this paper presents a transparent loss recovery scheme to transparently support the robust video transmission on behalf of real-time streaming video applications. The proposed scheme includes the following two modules: (i) a transparent QoS mechanism which enables the QoS setup of video applications without the requirement for any modification of the existing legacy applications through its use of an efficient packet redirection scheme; and (ii) an instant frame-level FEC technique which performs online FEC bandwidth allocation within TCP-friendly rate constraints in a frame-by-frame basis to minimize the additional FEC processing delay. The experimental results show that the proposed scheme achieves nearly the same video quality that can be obtained by the optimal frame-level FEC under varying network conditions while maintaining low end-to-end delay.
Copyright © 2008 Chi-Huang Shih et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. INTRODUCTION
As different types of wireless networks are converging into
the wired Internet,providing end-to-end quality of service (QoS) is essential for video transmission over the wireless Internet. Enabling QoS involves multidisciplinary solutions and the related areas could range from end-users applications to the underlying network architectures. Generally,the QoS support in wireless Internet can be achieved through the network-centric and the end-system centric approaches
[1].In the network-centric approach, the integrated service
(IntServ) [2] and the differentiated service (DiffServ) [3]
are two well-known architectures to support QoS provi-sioning for the Internet. IntServ and signaling protocols, such as reservation protocol (RSVP), provide the per-flow QoS guarantee. DiffServ provides both the guaranteed and
relative QoS by dividing packets into different service classes
and forwarding them as different priorities. For wireless
networks, there have been many studies related to the QoS provisioning such as the third generation partnership
project (3GPP) for UMTS networks [4], IEEE 802.11e
for wireless local area networks [5], and IEEE 802.16 for
wireless local and metropolitan area networks [6]. The
core wireless infrastructures need to provide prioritized
QoS services to support various applications with different
requirements.
On the other hand, the end-system centric approach is considered as a QoS enhancement solution without the underlying QoS architectures from the network. For the time-varying network conditions, the applications can employ the adaptive control mechanisms to minimize the impairment of video delivery caused by channel errors and congestions during transmission. Congestion control and error control are two main mechanisms to support the robust video transmission. Congestion control mechanism aims at reducing packet loss and delay due to network congestion. Error-control mechanism intends to combat the transmission errors by recovering lost data; for example, two popular error-control schemes are forward error correction (FEC) and automatic repeat request (ARQ).
To support end-to-end QoS over the wireless Internet, the applications are expected to have two following QoS enhancements: (1) the communication between end users and the underlying QoS architecture for QoS negotiation; and (2) the employment of network adaptive control
mechanisms to achieve the effective adaptation of network
conditions. The QoS-aware middleware has been introduced to render the application design independent from the underlying network QoS architecture and further achieve the
integration of different QoS solutions [7]. It is noted that
the major changes to both the end system and legacy video application are necessary, and therefore the deployment of QoS-enabled wireless video services could be slow and difficult. This problem can be solved by improving the degree of transparency to minimize the modifications of the
legacy applications. In [8], by adopting so-called QoS library
redirection (QLR), applications can set up QoS without source-code modification but the application-dependant library is required for all target applications individually.
In [9],a link-layer performance enhancing proxy (PEP)
is proposed to cope with the impairment introduced by wireless links over the Internet stack. However, the link-layer approach is inflexible to enable QoS for diverse applications with different requirements.
In this paper, a transparent QoS mechanism (TQM) is proposed to provide a flexible platform to transparently support QoS services. It is noted that this paper focuses on the provision of video services in IP-based wireless networks. Therefore, the proposed TQM transparently supports IP services by employing a packet redirection technique that operates based on the TCP/IP stack. Based on the technique of packet redirection, TQM performs the packet control operations to provide various QoS enhancements for the legacy applications. The proposed TQM aims at facilitating QoS deployment over the wireless Internet. The main features of TQM are: (1) end users can specify which target application receives what types of QoS enhancements and the target application is transparently to be QoS-aware without any modification; (2) no modifications are required to the existing Internet transport protocols; and (3) TQM supports diverse QoS enhancement modules (EM) and thus one can integrate various EMs to maximize the perceived video quality. In this paper, we primarily focus on designing an adaptive error-control scheme for TQM to cope with the varying wireless channel errors, and particularly its packet-level FEC enhancement module.
FEC introduces the redundancy that trades the additional bandwidth cost to protect video streams from wireless losses.
Unfortunately, the effectiveness of FEC decreases since the
redundancy cost could lead the self-induced congestion to
cause the adverse effects on video quality such as congestion
losses due to buffer overflow and the longer end-to-end
latency due to queueing delay [10,11]. This is significant
to the compressed VBR video source since it usually exhibits long-range dependence (LRD) with larger losses and/or delay
within a concentrated period [12]. Congestion losses impede
the successful loss recovery since the amount of packet losses induced by both wireless error and congestion might exceed the error correction capacity of FEC. The longer end-to-end
latency makes packet arrival useless to video decoder with the timing constraint. In addition, the failed redundancy and also the useless video data lead the unnecessary bandwidth waste, studied as the congestion collapse issues by recent
works, would degrade the network utility [13]. To enhance
the effectiveness of FEC, it is therefore necessary to consider
both the loss recovery aspects of FEC and the level of
network congestion. Park and Wang [10] consider the
optimal problem of designing an adaptive FEC protocol for real-time MPEG video transmission over the Internet with-out regard to TCP-friendly transmission rate constraints. Their proposed FEC-control method adapts the redundancy degree to perceived packet loss on the network. When increased redundancy results in a nonincreasing recovery performance due to the fact that selfcongestion impedes the timely recovery of video information, the adaptive FEC protocol exponentially decreases the redundancy degree to avoid adverse network effects on video quality. On the other
hand, Wu et al. [14] derive the analytical FEC model for a
TCP-friendly MPEG video stream with temporal scaling to obtain the optimal reconstruction quality in the presence of packet losses.In their model, FEC is applied to different types of video frames while the temporal scaling technique is used to adjust the stream data rate by discarding frames based on the frame dependency of MPEG video. Yuan et al.
[15] present an FEC model, which applies FEC at the group
of picture (GOP) level, to increase the error correction capacity of FEC for MPEG video streams within TCP-friendly constraints. Compared with the optimal frame-level
FEC technique proposed in [14] by Wu et al., this GOP-level
FEC technique requires more computational complexity in average to process a larger amount of video data. However, both techniques rely on the presence of a large buffer to collect an entire GOP for optimally calculating the FEC coding rate in the video sender. This results in a coding buffering delay on the order of GOP duration in the video receiver before the smooth video presentation begins. The coding buffering delay generally contributes to the overall end-to-end delay of video. Usually, the acceptable delay depends on the video applications. For interactive services, such as video conferencing, the end-to-end delay should not exceed 100 milliseconds to ensure good quality
[16].
In this paper, the design of FEC-on-TQM integrates sev-eral EMs to transparently support robust video transmission on behalf of real-time streaming video applications. FEC-on-TQM utilizes an instant frame-level FEC technique to
minimize the coding buffering delay experienced by the
user, while still maintaining near-highest video quality that the optimal frame-level FEC can obtain within the TCP-friendly rate constraints. In order to maintain high video quality with low delay, we first derive a model of video frame priorities based on the temporal dependency of MPEG video to distribute the available TCP-friendly bandwidth budget among video frames. Then the decision of applying FEC to video frames or discarding frames to match the TCP-friendly transmission rate is done on a frame-by-frame basis. To evaluate the performances of the proposed scheme, we constructed the experiments in a controlled network
environment. The experimental results show that the instant frame-level FEC technique achieves nearly the same video quality that can be obtained by the optimal frame-level FEC while maintaining low end-to-end delay of video. Based on the above mentioned techniques of packet redirection and instant frame-level FEC, FEC-on-TQM carries out the transparent loss recovery without any modification of legacy applications and obtains a high delivered video quality for low-delay video streaming services.
The remainder of this paper is organized as follows.
Section 2describes the basic operating principles of TQM,
while Section 3 describes the design of FEC-on-TQM and
its implementation issues. Section 4 reviews the instant
frame-level FEC control scheme. Section 5 presents and
discusses the experimental performance evaluation results.
Finally, Section 6 provides some brief concluding remarks
and indicates the intended direction of future research. 2. TRANSPARENT QoS MECHANISM (TQM)
2.1. TQM architecture
The TQM mechanism proposed in this study provides a transparent QoS enhancement for Internet multimedia applications. In order to improve its flexibility, TQM is designed for implementation at the application layer, and therefore enables existing Internet transport protocols to
operate in their usual way. As shown inFigure 1, TQM uses
two modules, that is, “Flow State” and “QoS Manager”, to accommodate the diverse characteristics of existing appli-cations and their underlying transport protocols. When an application is launched, some records are created in the kernel space for system communication and maintenance purposes. Without modifying either the kernel or the application, the virtual flow state module in TQM collects flow information from these records and presents this information to the QoS Manager. The user can then interact with the QoS Manager to specify directly those applications which require enhanced QoS support. Subsequently, the QoS Manager applies the flow information supplied by the flow state module to transparently carry out EM operations on behalf of the applications.
TQM achieves transparent QoS enhancement by means of a packet redirection scheme. Specifically, the implementa-tion of QoS-manager module is based on the funcimplementa-tionality
of the IP firewall [17] as well as the divert socket [18]. IP
firewall filters packets traveling up or down the IP stack; and it defines the target action on these filtered packets, according to firewall rules. Instead of specifying typical target actions such as ACCEPT or DENY, target DIVERT can redirect filtered packets to a divert socket. Divert socket is one element of general BSD socket and can be bound to a specific port of the host for IP packet interception and injection. Since the IP firewall is located at the bottom of the IP stack, it redirects the IP flows which are specified in divert messages received from the QoS manager to a specific system port. The QoS-manager module employs the divert socket to bind this specific port and then receives the IP data packets from it. Using the same port, the
EM-Host User space Application Socket API QoS manager EM Data flow Divert message Flow state Traffic control Divert socket Out In Kernel space
Figure 1: Basic components and operations of TQM host. Note that sender data flow is marked as “Out” (solid line) and receiver data flow is marked as “In” (dotted line).
processed IP packets are then injected back into the general network protocol stack and are subsequently processed using regular system routines. Accordingly, the packet redirection of TQM operates in a two-way manner. In the TQM sender, the QoS Manager redirects data flows generated by the applications and injects them into the protocol stack for network transmission. Conversely, in the TQM receiver, the QoS Manager redirects the data flows received from the underlying network infrastructure and then injects them into upper-layer applications.
Figure 2 presents a detailed illustration of the major components in the TQM QoS Manager. TQM provides a transparent QoS enhancement capability through the use of data planes and control planes. In the data plane, user-specific flows are identified and the related flow informa-tion is passed to the underlying EM. Executing the flow information management function in the data plane involves three separate components, namely the application filter, the user interface, and the application list. Briefly, the application filter collects the flow information relating to launched applications from the flow state module, and users monitor their applications on the application list through the user interface. By accessing the user interface, a user can view those flows which have been launched. Subsequently, he or she can specify the particular flow (or flows) for which enhanced QoS support is required. By querying the application list, the application filter detects and discards any flow information relating to applications which have not been specified by a user.
Application list
Some applications, for example, DNS query, time services, http services, and so forth, do not have strict QoS require-ments. Therefore, TQM uses the application list to indicate the multimedia applications for which the user specifies that QoS enhancement support may be required. The user selects
Flow information Application filter Data plane C o nt ro l p lane User space Kernel space
Flow state Traffic control QoS manager User interface 1 EM 2 Divert message 3 IP packet 4 EM-processed IP packet Data flow Application list vic vls · · · Figure 2: TQM architecture.
these specific applications on the application list via the user interface, and can arbitrarily add or delete selection records on demand. By accessing this list, the application filter can indicate to the flow state module the specific applications for which it should collect flow information.
Application filter
Using the flow state module, the application filter retrieves the flow information required to transparently start QoS sessions on behalf of the applications. Generally, this infor-mation is related to five tuples (i.e., the transport protocol, the source IP address, the source port, the destination port, and the destination address).When a user requests support for a specific flow, this information is passed to the control plane, which then establishes the QoS session.
In the control plane, the EMs set up QoS sessions on behalf of the legacy applications. Importantly, the control plane in the proposed TQM mechanism contains many
QoS EMs of different types. These EMs may be used
either separately or cooperatively in order to carry out various functions. Consequently, TQM provides a flexible
and efficient mechanism for the QoS enhancement of diverse
applications. Based on the flow information received from the data plane, the EM identifies the user-specified flow and then intercepts the corresponding IP packets to carry out the QoS enhancement process. The EM-processed packets are then returned to the network protocol stack for further processing. Note that IP flow packets may be added, deleted or modified during QoS enhancement depending on the particular EM type.
2.2. EM types
TQM comprises a collection of different EMs to support video applications with diverse QoS requirements. Indi-vidual EMs may function independently for a specific IP
flow or may cooperate with other EMs to improve the
overall QoS. The TQM architecture is sufficiently flexible to
support both the addition of new EMs and updates of the algorithms upon which the EMs are based. According to the general QoS solutions ranging from the underlying network architectures to upper applications, there are three basic examples of EM to support the QoS requirements of wireless video transmissions, namely, packet mapping control, TCP-friendly congestion control, and adaptive error control. These three EMs are discussed briefly in the following subsections.
2.2.1. Packet mapping control EM
In general, QoS architectures employ some form of traffic
category concept to support the implementation of scalable
and manageable wireless networks with service di
fferentia-tion capabilities. For example, the 3GPP working group has
defined four different QoS classes, that is, conversational,
streaming, interactive and background, based on a
consider-ation of the delay sensitivity of different applications.
Mean-while, the 802.11e standard prescribes eight different traffic
categories for wireless local area networks. Similarly, the 802.16 standard defines four different types of service flow in wireless metropolitan area networks, namely, unsolicited grant service, real-time polling service, nonreal-time polling service, and best effort service.Through application-specific QoS mapping mechanisms, data flows are assigned to the appropriate traffic category and are then transmitted with the corresponding priority. Furthermore, individual video packets may also be categorized in accordance with their loss and delay properties; and then assigned to different prioritized transmission classes in order to optimize the video quality under given rate or cost constraints.
To accommodate these various strategies, it is necessary
within single flow. The general differentiation parameters include the IP source/destination address, the protocol, the source/destination port number, and the type of service (TOS) value. In the proposed TQM mechanism, the packet mapping control EM maps the flows identified by the Flow
State module to user-specified traffic categories. Meanwhile,
differentiation within a single flow is achieved without
modifying the applications by transparently diverting the IP packets to the packet mapping control EM. The packet mapping control EM first identifies the packet using appro-priate classification criteria (e.g., video frame/layer type) and then maps the packet to the appropriate prioritized class. In mapping the packet to the prioritized class, the EM either directly forwards the identified packets to the underlying network architecture or marks packets with the proper TOS value according to the criteria specified by the underlying architecture before packet forwarding.
2.2.2. TCP-friendly congestion control EM
Since the capacity of a wireless channel is scarce and time-varying, bursty losses and excessive delays caused by network congestion can significantly degrade the perceived video quality. Accordingly, congestion control mechanisms aim to minimize the impairment of the delivered video quality caused by network congestion by reducing packet losses and delays. Additionally, video streams must share the available bandwidth equally with other TCP-based flows. Based on the QoS requirements of multimedia transport, TCP-friendly congestion control mechanisms smoothly adjust the transmission rate and avoid increasing the latency by not retransmitting lost packets. Two types of TCP-congestion control mechanism are generally used for multimedia appli-cations, namely sender-based rate adjustment and model-based rate adjustment. The former mechanism is similar to TCP in that it performs additive increase and multiplicative
decrease (AIMD) control at the sender end [19]. Conversely,
the latter scheme uses a throughput equation based on a stochastic TCP model to adjust the transmission rate as a function of the loss event rate and the round trip time (RTT)
[20,21].
To transport video in a TCP-friendly manner, video applications must match the output rate to the available network bandwidth, as estimated by a TCP-friendly con-gestion control protocol. One approach for achieving TCP-friendly video transmission is for the video application to use a rate control scheme to regulate the coded bit stream under the constraint of certain given conditions while simultaneously maximizing the user’s perception of
the media stream quality [22,23]. However, this approach
generally requires modification of the original legacy applica-tions. An alternative approach is to control the transmission rate of the encoded video stream packets so as to obtain a tradeoff between the degree of TCP-friendliness and
the perceived media quality [24]. The TQM mechanism
proposed in this study constructs a TCP-friendly congestion control protocol which operates totally transparently to the legacy applications and supports these two approaches to achieve the rate matching. In the first approach, the
video transcoding can be used to adapt the output video
stream to the available TFRC bandwidth [25]. The general
video transcoding parameters consist of frame size, frame rate, and quantization value. In the second approach, the TCP-friendly congestion control EM uses a simple packet control mechanism to either discard packets according to the priority classes they belong to or, if the output rate of the video streams exceeds the TCP-friendly sending rate, to postpone packet transmission until the next transmission period.
2.2.3. Adaptive error control EM
Error-control schemes aim at coping with the wireless error problems for high-bit-rate video transmissions over error-prone wireless networks. Typical examples of error control schemes presented in the literature include ARQ and FEC. The objective of both schemes is to obtain a higher data throughput by recovering corrupted packets. However, the two schemes adopt different strategies to achieve this objective. Specifically, ARQ retransmits lost packets, whereas FEC deliberately generates redundant data to enable the reconstruction of any video data which is lost during transmission. Although it has been shown that ARQ is more effective than FEC, its end-to-end retransmissions may impede the timely presentation of video content. Therefore, the FEC scheme is generally preferred for real-time video applications. Since the loss condition changes dynamically in wireless environments, and furthermore, the self-induced congestion caused by the generation of excessive
redundant data has an adverse effect on the video quality,
it is desirable for FEC-control schemes to have the ability to adapt dynamically to varying network conditions, that is, to changes in the packet loss rate or the level of network congestion. Therefore, the adaptive error control EM in the proposed TQM mechanism installs the FEC encoder in the data sender and the FEC decoder in the data receiver to support the transparent FEC coding of video applications.
The current study focuses primarily on the integration of these three EMs on the proposed TQM to carry out the robust video transmission and the testing of an instant frame-level FEC technique to adjust the number of redun-dant packets according to the network conditions.
3. FEC-ON-TQM
3.1. Overall structure
The aim of FEC-on-TQM is to enhance the perceived quality of the delivered video by transparently recovering packet losses on behalf of legacy video applications with no FEC capability. In this study, FEC-on-TQM integrates
three EMs mentioned in Section 2.2 to achieve a tradeoff
between robust video transmissions and efficient bandwidth
utilization. Figure 3 illustrates the end-to-end
communi-cation between two TQM hosts. In the video sender, the packet mapping EM identifies the frame type of video packets redirected by the QoS manager and forwards a stream of video frames to the FEC EM. The FEC EM applies
delays incurred along the path between the sender and the receiver, and (2) FEC-on-TQM successfully integrates several EMs to carry out the proposed instant frame-level FEC technique, resulting in a low-delay, good-visual-quality video experience. Future studies include the testing of network-adaptive video applications, such as Helix video server, on FEC-on-TQM, and the integration of EMs to further improve the overall QoS of diverse multimedia applications over the wireless Internet.
REFERENCES
[1] J. Wroclawski, The use of RSVP with IETF Integrated Services, RFC 2210, September 1997.
[2] S. Black, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weiss, “An architecture for differentiated services,” RFC 2475, December 1998.
[3] Q. Zhang, W. Zhu, and Y.-Q. Zhang, “End-to-end QoS for video delivery over wireless Internet,” Proceedings of the IEEE, vol. 93, no. 1, pp. 123–133, 2005.
[4] Quality of service (QoS) concept and architecture, 3GPP TS 23.107, September 2003.
[5] IEEE Standard, “Local and metropolitan area networks— specific requirements part 11: wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment 8: medium access control (MAC) quality of Service enhancements,” pp.1–189, 2005.
[6] IEEE Standard, “802.16-2004 part 16: air interface for fixed broadband wireless access systems,” October 2004.
[7] K. Nahrstedt, D. Xu, D. Wichadakul, and B. Li, “QoS-aware middleware for ubiquitous and heterogeneous environments,”
IEEE Communications Magazine, vol. 39, no. 11, pp. 140–148,
2001.
[8] Y.-B. Miao, W.-S. Hwang, and C.-K. Shieh, “A transparent deployment method of RSVP-aware applications on UNIX,”
Computer Networks, vol. 40, no. 1, pp. 45–56, 2002.
[9] L. Munoz, M. Garcia, J. Choque, R. Aguero, and P. Mahonen, “Optimizing Internet flows over IEEE 802.11b wireless local area networks: a performance-enhancing proxy based on forward error correction,” IEEE Communications Magazine, vol. 39, no. 12, pp. 60–67, 2001.
[10] K. Park and W. Wang, “QoS-sensitive transport of real-time MPEG video using adaptive redundancy control,” Computer
Communications, vol. 24, no. 1, pp. 78–92, 2001.
[11] O. Ait-Hellal, E. Altman, A. Jean-Marie, and I. A. Kurkova, “On loss probabilities in presence of redundant packets and several traffic sources,” Performance Evaluation, vol. 36-37, pp. 485–518, 1999.
[12] K. Park and W. Willinger, Self-Similar Network Traffic and
Performance Evaluation, Wiley-Interscience, New York, NY,
USA, 2000.
[13] S. Floyd and K. Fall, “Promoting the use of end-to-end congestion control in the Internet,” IEEE/ACM Transactions on
Networking, vol. 7, no. 4, pp. 458–472, 1999.
[14] H. Wu, M. Claypool, and R. Kinicki, “Adjusting forward error correction with temporal scaling for TCP-friendly streaming MPEG,” ACM Transactions on Multimedia Computing,
Com-munications, and Applications, vol. 1, no. 4, pp. 315–337, 2005.
[15] Y. Yuan, B. F. Cockburn, T. Sikora, and M. Mandal, “Efficient allocation of packet-level forward error correction in video streaming over the Internet,” Journal of Electronic Imaging, vol. 16, no. 2, Article ID 023012, 12 pages, 2007.
[16] M. Baldi and Y. Ofek, “End-to-end delay analysis of videocon-ferencing over packet-switched networks,” IEEE/ACM
Trans-actions on Networking, vol. 8, no. 4, pp. 479–492, 2000.
[17] Linux IP firewall HOWTO,, http://www.linuxfaq.com /LDP/ HOWTO/Firewall-HOWTO.html.
[18] Divert sockets,http://tldp.org/HOWTO/Divert- Sockets-min-i-HOWTO.html#toc6.
[19] R. Rejaie, M. Handley, and D. Estrin, “Quality adaptation for congestion controlled video playback over the Internet,” ACM
SIGCOMM Computer Communication Review, vol. 29, no. 4,
pp. 189–200, 1999.
[20] S. Floyd, M. Handley, J. Padhye, and J. Widmer, “Equation-based congestion control for unicast applications,” in
Pro-ceedings of the ACM Annual Conference of the Special Interest Group on Data Communication (SIGCOMM ’00), pp. 43–56,
Stockholm, Sweden, August-September 2000.
[21] M. Handley, S. Floyd, J. Pahdye, and J. Widmer, “TCP friendly rate control (TFRC): protocol specification,” RFC3448, Jan-uary 2003.
[22] D. Wu, Y. T. Hou, W. Zhu, et al., “On end-to-end architecture for transporting MPEG-4 video over the Internet,” IEEE
Transactions on Circuits and Systems for Video Technology,
vol. 10, no. 6, pp. 923–941, 2000.
[23] Q. Zhang, W. Zhu, and Y.-Q. Zhang, “Resource allocation for multimedia streaming over the Internet,” IEEE Transactions on
Multimedia, vol. 3, no. 3, pp. 339–355, 2001.
[24] Z. Wang, S. Banerjee, and S. Jamin, “Media-friendliness of a slowly-responsive congestion control protocol,” in Proceedings
of the 14th International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV ’04),
pp. 82–87, Cork, Ireland, June 2004.
[25] V. Samata, R. Olivera, A. Dixit, P. Aghera, P. Zerfos, and S. Lu, “Impact of video transcoding parameters on dynamic video transcoding,” in Proceedings of the 1st International Conference
on Communication System Software and Middleware (COM-SWARE ’06), New Delhi, India, January 2006.
[26] V. Arya and T. Turletti, “Accurate and explicit differentiation of wireless and congestion losses,” in Proceedings of the 23rd
International Conference on Distributed Computing Systems (ICDCS ’03), Providence, RI, USA, May 2003.
[27] S. Biaz and N. Vaidya, “Discriminating congestion losses from wireless losses using inter-arrival times at the receiver,” in
Proceedings of the IEEE Symposium on Application-Specific Systems and Software Engineer (ASSET ’99), pp. 10–17,
Richardson, Tex, USA, March 1999.
[28] Y. Tobe, Y. Tamura, A. Molano, S. Ghosh, and H. Tokuda, “Achieving moderate fairness for UDP flows by path-status classification,” in Proceeding of the 25th Annual IEEE
Con-ference on Local Computer Networks (LCN ’00), pp. 252–261,
Tampa, Fla, USA, November 2000.
[29] H. Schulzrinne, S. Casner, R. Frederick, and V. Jacobson, “RTP: a transport protocol for real-time applications,” RFC 3550, July 2003.
[30] F. Borgonovo and A. Capone, “Efficiency of error-control schemes for real-time wireless applications on the Gilbert channel,” IEEE Transactions on Vehicular Technology, vol. 54, no. 1, pp. 246–258, 2005.
[31] A. Li, “RTP payload format for generic forward error cor-rection,” Internet Draft draft-ietf-avt-ulp-18, IETF, work in progress, June 2006.
[32] J. Rosenberg and H. Schulzrinne, “An RTP payload format for generic forward error correction,” RFC 2733, December 1999.
[33] J. Padhye, V. Firoiu, D. F. Towsley, and J. F. Kurose, “Modeling TCP reno performance: a simple model and its empirical validation,” IEEE/ACM Transactions on Networking, vol. 8, no. 2, pp. 133–145, 2000.
[34] S. Gringeri, R. Egorov, K. Shuaib, A. Lewis, and B. Basch, “Robust compression and transmission of MPEG-4 video,” in Proceedings of the 7th ACM International Conference on
Multimedia (MULTIMEDIA ’99), pp. 113–120, Orlando, Fla,
USA, October 1999.
[35] The VideoLAN server,http://www.videolan.org/vlc/streaming .html.
[36] S. Cen, P. C. Cosman, and G. M. Voelker, “End-to-end differentiation of congestion and wireless losses,” IEEE/ACM
