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«End-to-End QoS Provision over Heterogeneous IP and non IP Broadband Wired and Wireless Network Environments A dissertation submitted in satisfaction ...»

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The DiffServ routers implement WRED queue management. In this scenario, according to Figure 6.4, the overall PSNR is better than without using prioriti¯ zation. The Pavg value is 31.054dB. Figure ?? depicts the SSIM metric of both scenarios (BE and QoS-enabled networks) for foreman video sequence.

Figure 7.3: Scalable video transmission over DiffServ/802.

11e Heterogeneous Network The same measurement is repeated, but for four different YUV video sequences consisting of 300 to 2000 frames. For all the scenarios, we consider the simple but efficient error concealment scheme described in the previous section.

The average P SN R and SSIM for the above scenarios are shown in Table 6.4,

where:

• in Scenario 1 I transmit scalable MPEG-4 FGS video stream in a best effort network.

• in Scenario 2 I transmit scalable MPEG-4 FGS video stream over a DiffServ/802.11e heterogeneous network.

As it seems in Table 7.2, the proposed scheme improves the overall quality of the received video. By adapting the trasmission rate of EL, we can increase not Figure 7.4: SSIM measurements of scalable video transmission over DiffServ/802.11e Heterogeneous Networks only the overall video quality in terms of P SN R but also, as Figures ?? and 7.3 show, the quality variations were being minimized.

7.5 Conclusions Nowadays, continuous media applications over heterogeneous all-IP networks, such as video streaming and videoconferencing, become very popular. Several approaches have been proposed in order to address the end-to-end QoS both from the network perspective, like DiffServ and 802.11e access categories, and from the application perspective, like scalable video coding and packetized prioritization mechanisms. In this chapter, I address the end-to-end QoS problem of scalable video streaming traffic delivery over a heterogeneous DiffServ/802.11enetwork.

It proposes and validates through a number of NS2-based simulation scenarios a framework that explores the joint use of packet prioritization and scalable

–  –  –

video coding, by evaluating scalable extension of H.264/MPEG-4 AVC, together with the appropriate mapping of 802.11e access categories to the DiffServ traffic classes. The proposed prioritization scheme in conjuction with the proposed DiffSer/802.11e classes coupling have improvements in the overall quality of the received video, by isolating the losses and the delays to packets carrying less important partitions.

–  –  –

The transmission of multimedia content over IP networks both fixed and wireless has been growing steadily over the past few years and is expected to continue growing. Meanwhile, the quality of streaming multimedia, in general and video, in particular, can be improved. To this context, the thesis discusses an integrated framework for QoS provision for multimedia delivery over IP networks with related cross-layer concepts.

The thesis reviews solutions which address the problem of end-to-end quality of service for multimedia streaming applications over heterogeneous networks, including wireless and wired network domains. It presents advanced QoS-enabled multimedia streaming techniques from the application perspective. These include scalable video coding, packet prioritization and packetization. Following this, it describes described a number of available network technologies that have support to class based quality of service from the network perspective, including the DiffServ model, DVB Bandwidth Management approach, UMTS QoS Architecture and IEEE 802.11e.

From a video coding point of view, scalability plays a critical role in delivering the best possible video quality over unpredictable heterogeneous networks. Video scalability enables an application to adapt the streamed video quality to changing network conditions (and specifically to bandwidth variation) and device complexities. The basic coding scheme for achieving a wide range of spatio-temporal and quality scalability can be classified as scalable video codec. For Signal-to-Noise Ratio (SNR) scalability two approaches are the most appropriate for video delivery over heterogeneous networks, the MPEG-4 Fine Grain Scalability (FGS) video coding and the scalable extension of H.264/MPEG-4 AVC. The FGS feature of MPEG-4 is a promising scalable video solution to address the problem of guaranteed end-to-end QoS provision concerning the application perspective.

According to MPEG-4 FGS, the Base Layer (BL) provides the basic video quality to meet the minimum user bandwidth, while the Enhancement Layer (EL) can be truncated to meet the heterogeneous network characteristics, such as available bandwidth, packet loss, and delay/jitter. In order to support fine-granular SNR scalability, progressive refinement (PR) slices have been introduced in the scalable extension of H.264. A base representation of the input frames of each layer is obtained by transform coding similar to H.264, and the corresponding Network Abstraction Layer (NAL) units (containing motion information and texture data) of the base layer are compatible with the single layer H.264/MPEG-4 AVC. The quality of the base representation can be improved by an additional coding of so-called PR slices. The corresponding NAL units can be arbitrarily truncated in order to support fine granular quality scalability or flexible bit-rate adaptation.





For real time multimedia streaming applications, packet prioritization is performed in such a way to reflect the influence of each stream or packet to the end-to-end delay. Packets will be classified by the context aware applications in the granularity of session, flow, layer and packet. The most important QoS parameters, rate, delay and error are used to associate priority for delay and loss.

The bandwidth (rate) is usually mapped with the scalable coding mechanism such as MPEG-4 FGS. Most of the available prioritization techniques are based on granularity of session, flow and layer. The per-flow prioritization is based on the user-based allocation within an access network. Lots of prioritization for the Unequal Error Protection is mapped better with the layered differentiation as described with object scalability. The session-based prioritization is a better way to prioritize packets based on delay.

The thesis contributes towards QoS mapping control schemes between different network technologies that support QoS and service differentiation. The common operation of IP DiffServ and DVB BM mechanisms can offer quality gains for media delivery across heterogeneous IP/DVB settings. The mapping among the traffic classes of IP DiffServ and UMTS network domains is studied. In order to address the end-to-end QoS problem of scalable video streaming traffic delivery over a heterogeneous DiffServ/802.11e network, the thesis proposes a framework that explores the joint use of packet prioritization and scalable video coding, by evaluating scalable extension of H.264/MPEG-4 AVC, together with the appropriate mapping of 802.11e access categories to the DiffServ traffic classes. The proposed prioritization scheme in conjunction with the proposed DiffSer/802.11e classes coupling have improvements in the overall quality of the received video, by isolating the losses and the delays to packets carrying less important partitions.

The development of pricing schemes that account for the specific challenges in streaming video to wireless clients is one of the key requirements for making wireless video services economically viable. In this concept, the thesis proposes a framework for the pricing of video streaming over heterogeneous networks that support QoS and Service differentiation, based on the cost of providing different levels of quality of service to different classes. Pricing of network services dynamically based on the level of the service, usage and congestion allows a more competitive price to be offered, and allows network to be used more efficiently. The proposed framework incorporates the quality of the delivered video in the given networking context into a dynamic service negotiation environment, in which service prices increase in response to congestion, the applications adapt to price increases by adapting their sending rate and/or choice of service. The thesis discusses models in the context of IP DiffServ for the wired domain and cellular and WLAN for the wireless domain.

There are many challenges for future work on pricing, especially for wireless video services. The development of the parameter setting in the utility model (both from the user and network perspective) and how this can benefit the overall video quality, it is still an open issue. Another interesting open issue, is the pricing when the users have the options of a different path or provider.

–  –  –

[1] ISO/IEC-N5231, “Mpeg-21 overview v.5,” no. N5231, 2002.

[2] M. Ghanbari, Standard Codecs: Image Compression to Advanced Video Coding. United Kingdom: IEE Telecommunications Series 49, IEE Publications, June 2003.

[3] I. DIS 13818-2, “Generic coding of moving pictures and assiciated audio information, (mpeg2),” ISO/IEC DIS 13818-2, 1995.

[4] W. Li, “Overview of fine granularity scalability in mpeg-4 video standard,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 3, Mar. 2001.

[5] ISO/IEC-14496-2, “Coding of audio-visual objects - part 2: Visual,” final committed draft, May 1998.

[6] H. M. Radha, M. van de Schaar, and Y. Chen, “The mpeg-4 fine grained scalable video coding method for multimedia streaming over ip,” IEEE Transactions on Multimedia, vol. 3, no. 1, Mar. 2001.

[7] K. Shen and E. J. Delp, “Wavelet based rate scalable video compression,” IEEE Transactions Circuits Systems in Video Technology, vol. 9, no. 1, pp.

109–122, Mar. 1999.

[8] Q. Zhang, G. Wang, W. Zhu, and Y.-Q. Zhang, “Robust scalable video streaming over internet with network adaptive congestion control and unequal loss protection,” in Proceedings of Packet Video Workshop, May 2001.

[9] J. Shin, J-G.Kim, J. Kim, and C.-C. Kuo, “Dynamic qos mapping framework for relative service differentiation-aware video streaming,” IEEE Transactions on Multimedia, vol. 3, no. 2, pp. 219–231, June 2001.

[10] X. Wu, S. Cheng, and Z. Xiong, “On packetization of embedded multimedia bitstreams,” IEEE Transaction on Multimedia, vol. 3, no. 1, Mar. 2001.

[11] S. Blake, D. Black, M. Carlson, E. Davies, Z. Wang, and W. Weisss, “An architecture for differentiated service,” IETF RFC 2475, December 1998.

[12] K. Nichols, S. Blake, F. Baker, and D. Black, “Rfc2474:definition of the differentiated services field (ds field) in the ipv4 and ipv6 headers,” Dec.

1998.

[13] J. Heinanen, F. Baker, W. Weiss, and J. Wroclawsk, “Assured forwarding phb group,” IETF RFC2597, December 1999.

[14] I. Stoika and H. Zhang, “Providing guaranteed services without per flow management,” in Proceedings of ACM SIGCOMM, Boston, MA, 1999.

[15] I. Stoika, S. Shenker, and H. Zhang, “Core-stateless faur queueing: Achieving approximately fair bandwidth allocations in high speed networks,” in Proceedings of ACM SIGCOMM, Vancouver, Canada, 1998.

[16] TS25.401, “R99, utran overall description.” [17] H. Kaaranen, A. Ahtiainen, L. Laitinien, S. Naghian, and V. Niemi, UMTS

NETWORKS: Architecture, Mobility and Services. West Sussex, England:

John Wiley and Sons Ltd, 2001.

[18] P. Massam, MANAGING SERVICE LEVEL QUALITY ACROSS WIRELESS AND WIRED NETWORKS. West Sussex, England: John Wiley and Sons Ltd, 2001.

[19] F. Kelly, S. Zachary, and I.B.Ziedins, Stochasti Networks: Theory and Applications, Royal Statistical Society Lecture Note Series. Oxford University Press, 1996, pp. 141–168.

[20] [21] J. S. Daniel, D. C. Lee, and C.-C. J. Kuo, Quality of Service for Internet Multimedia. Upper Saddle River, New Jersey: Prentice Hall, 2003.

[22] T. Ahmed, A. Naffaa, and A. Mehaou, “An object based mpeg4 multimedia content classification model for ip qos differentiation,” in Proceeding of the Internation Packet Video Workshop, PVW-2000, Cagliari, Italy, May 2000.

[23] D. T. McWherter, J. Sevy, and W. C. Regli, “Building an ip network qualityof-service testbed,” IEEE Internet Computing, vol. 04, no. 4, pp. 65–73, 2000.

[24] H. Bai, M. Atiquzzaman, and W. Ivancic, “Achieving end-to-end qos

in the next generation internet: Integrated services over differentiated service networks,” Tech. Rep., Mar. 2001. [Online]. Available:

http://gltrs.grc.nasa.gov/reports/2001/TM-2001-210755.pdf

–  –  –

[26] Digital video broadcasting (DVB); DVB specification for data broadcasting, ETSI EN Std. 301 192 v1.2.1, May 1999.

[27] W. Li, “Overview of fine granular scalability in mpeg-4 video standard,” IEEE Transaction on Circuits and Systems for Video Technology, vol. 11, no. 3, pp. 301–317, 2001.

[28] P. de Cuetos, M. Reisslein, and K. W. Ross, “Evaluating the streaming of fgs-encoded video with rate-distortion traces,” Institute Eurecom Technical Report, Tech. Rep. RR-03-078, 2003.

[29] H. M. Radha, M. van de Schaar, and Y. Chen, “The mpeg-4 fine grained scalable video coding method for multimedia streaming over ip,” IEEE Transaction on Multimedia, vol. 3, no. 1, pp. 53–68, 2001.

[30] P. Seeling, P. de Cuetos, and M. Reisslein, “Fine granularity scalable video:

Implications for streaming and a trace based evaluation methodology,” IEEE Communication Magazine, vol. 43, no. 4, pp. 138–142, 2005.

[31] J. Wang, M. Zhou, and Y. Li, “Survey on the end-to-end internet delay measurements,” Lecture Notes in Computer Science, vol. 3079, pp. 155–166, Jan. 2004.

[32] Z. Wang, L. Lu, and A. Bovik, “Video quality assesment based on structural distortion measurement,” Signal Processing: Image Communication, special issue on Objective Video Quality Metrics, vol. 19, no. 2, pp. 121–132, Feb.

2004.

[33] A. M. Rohaly, “Video quality experts group: Current results and future directions,” in Proceeding SPIE Visual Communications and Image Processing, vol. 4067, pp. 742–753, 2000.

[34] A. M. R. et al., “Video quality experts group: Current results and future directions,” in Proc. SPIE Visual Communications and Image Processing, Perth, Australia, june 2000, pp. 742–753.



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