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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 transmitter and the receiver reside on the same system (PC) in order to avoid issues that arise from synchronization errors or/and differences in system clocks [31]. The video traffic is transmitted from the source network interface, which is connected at the ingress router of autonomous system AS1, passes through the three different network domains and is finally returned back to the source system. For each generated packet, identified by a unique sequence number, the departure and arrival timestamps, and the type of payload that contains, are obtained. When a packet does not reach the destination, it is counted as a lost one. It is not only of interest the amount of lost packets, but also the type of content that packets have in their payload. Furthermore, not only the actual loss is important for the perceived video quality, but also the delay of packets/frames and the variation of the delay, usually referred to as packet/frame jitter. The packet/frame jitter can be addressed by so called play-out buffers. These buffers have the purpose of absorbing the jitter introduced by the network delivery delays. It is obvious that a big enough play-out buffer can compensate any amount of jitter. There are many proposed techniques in order to develop efficient and optimized play-out buffer, dealing with this particular trade-off. These techniques are not within the scope of the described testbed. For our experiments the play-out buffer is set to 1000ms.

In order to measure the improvements in video quality by employing H.264/MPEGAVC, I use the Peak Signal to Noise Ratio (PSNR) and the Structural Similarity (SSIM) [32] metrics. P SN R is one of the most widespread objective metric for quality assessment and is derived from the Mean Square Error (MSE) metric, which is one of the most commonly used objective metrics to assess the application level QoS of video transmissions [33].

Let’s consider that the video sequence is represented by V (n, x, y) and Vor (n, x, y), where n is the frame index and x and y are the statial coordinates. The average P SN R of the decoded video sequence among frames at indices between n1 and

n2 is given by the following equation:

–  –  –

where V denotes the maximum greyscale value of the luminance. The average M SE of the decoded video sequence among frames at indices beteen n1 and n2

is given by:

–  –  –

Note that, the P SN R and M SE are well-defined only for luminance values.

As it mentioned in [34], the Human Visual System (HVS) is much more sensitive to the sharpness of the luminance component than that of the chrominance component, therefore, I consider only the luminance P SN R.

SSIM is a Full Reference Objective Metric [35] for measuring the structural similarity between two image sequences exploiting the general principle that the main function of the human visual system is the extraction of structural information from the viewing field. If v1 and v2 are two video signals, then the SSIM

is defined as:

–  –  –

This section evaluates the performance of the proposed testbed configuration through a set of four experimental cases. In this chapter, I study the performance of our framework by enabling or disabling scalable video coding, or by enabling or disabling prioritized transmission. The quality gains of scalable video coding in comparison with non-scalable video coding and the quality gains of prioritized transmission in comparison with non-prioritized transmission are compared in detail.

The first experiment refers to a single layer MPEG-4 video stream transmission, where both DiffServ and BM mechanisms are not applied to the heterogeneous IP/DVB testbed. The second experiment refers to a scalable MPEG-4 FGS stream transmission of two layers, with both DiffServ and BM mechanisms deployed to the heterogeneous IP/DVB testbed. The BL packets are encoded using the MPEG4-FGS codec with MPEG2-TM5 rate control at 256 Kbps and the EL one encoded at 256 Kbps. By assigning high priority, Premium Service (EF) to BL, anyone can guarantee proper reception of the BL and without losses.

For the EL, I assign priorities according to the anticipated loss impact of each packet on the end-to-end video quality (considering the loss impact to itself and to dependencies). Each layer has a priority range, and each packet has different priority according to its payload. The packets, which contain data of an I-frame are marked with the lowest drop probability (AF11), the packets which contain data of a P-frame are marked with medium drop probability (AF12) and the packets which contain data of a B-frame are marked with high drop probability (AF13). The third experiment refers to a scalable MPEG4 video stream transmission consisting of one BL and two ELs (i.e., EL1 and EL2). The encoding of BL packets remains at 256 Kbps as in the second case, while the encoding of

–  –  –

packets of both ELs is at 256Kbps. For this case, I use EF for transmitting BL, AF11 for transmitting EL1, and Best Effort (BE) for transmitting EL2. For this case both DiffServ and BM mechanisms are active as in the second experiment.

Finally, the fourth experiment adopts the setup of the third case, while it applies the prioritized packetization scheme of the second experiment to the packets of the first EL (i.e., for this case, I use EF for transmitting BL).





Table 3.3 depicts the experimentation results in terms of PSNR and SSIM video quality metrics for eight different video sequences.

It is obvious that for the second experimentation there is a significant gain in video quality of 2.3dB in terms of PSNR when compared to the first scenario. In some video sequences with many differences between scenes the video quality gain is more that 3dB.

Furthermore, in the third experiment, it is observed a gain in video quality of

1.2dB, compared to the second experiment. At the fourth scenario, the video quality, in terms of PSNR, remains at the same level.

For the Highway video sequence (consisting of 2000 frames), I measure the packet/frame losses for I-, P-, and B-frames for the four experimental cases with results presented in Table 3.4.

By isolating the losses and the delays to P- and B- frames, can be achieved significant gains to video quality. Packet losses, which P-frame content, can affect not only the decoding process of P-frames but also the B-frames. This lead to higher percentages of B-frame losses but it is a significant affect to the overall video quality. In the fourth scenario, the user can achieve the same video quality, compared to third scenario, without using only the AF11 traffic class of the DiffServ. By distributing the traffic to all traffic classes, achieving the same video quality, in the lowest price, by sending lowest traffic to the cost effective AF11 traffic. From the network provider perspective, the providers network can use more efficient its bandwidth, by serving more users, at the level of quality they pay.

3.5 Conclusions

This chapter presents that the common operation of IP DiffServ and DVB BM mechanisms can offer quality gains for media delivery across heterogeneous IP/DVB settings. In this context, this study could constitute a potential vehicle for endto-end QoS provision. Towards this purpose, this chapter presents experimental results of an empirical study of a Linux-based heterogeneous IP/DVB network supporting continuous media applications. The development of new service categories increases the need for a differentiated, at the network level, treatment of the information packets, based on their different association with each type of service. This brings forward the concept of differentiated QoS provisioning, that is, the possibility to guarantee the most suitable service level for every traffic

–  –  –

category.

Several issues remain open and are currently under research. For example, a more efficient mechanism for prioritized packetization of video bit stream is required. Moreover, the distribution of packet priority and a price mechanism according to DS level remains to be examined.

–  –  –

Scalable Video Streaming traffic delivery in IP/UMTS networking environment

4.1 Introduction Fixed and wireless/mobile operators are faced with the challenge of a) both creating and delivering attractive IP-based multimedia services quickly responding to fast-changing business and customer demands, and b) evolving their current underlying networking infrastructure to an architecture that can deliver such services in a highly adaptable manner with guaranteed end-to-end Quality of Service (QoS) considering networking and application aspects.

At the same time, the customer side is offered IP connectivity via a wide variety of mobile/wireless access technologies. These technologies include: mobile communication networks, such as GPRS [36] and UMTS [37], the family of broadband radio access networks, like IEEE 802.11 [38] and HIPERLAN [39] and wireless broadcasting technologies, like digital video broadcasting (DVBsatellite and terrestrial) [26].

IP technology seems to be able to resolve the inter-working amongst the diverse fixed core and wireless/mobile access technologies at the network level.

For an all-IP network, the end-to-end QoS provision concerning the network perspective could be established through the appropriate mapping amongst the QoS traffic classes/services supported by the contributing underlying networking technologies. Building on this context, this work involves a DiffServ-aware IP core network and a UMTS access network and examines end-to-end QoS issues regarding scalable video streaming traffic delivery over such a network.

The Differentiated Services (DiffServ) [11] approach proposed by IETF supports (based on the DiffServ Code Point (DSCP) field of the IP header) two different services, the Expedited Forwarding (EF) that offers low packet loss and low delay/jitter and the Assured Forwarding (AF), which provides QoS guarantees better than the best-effort service. Differences amongst AF services imply that a higher QoS AF class will give a better performance (faster delivery, lower loss probability) than a lower AF class [13].

The QoS provision in UMTS is achieved through the concept of bearers. A bearer is a service providing a particular QoS level between two defined points invoking the appropriate schemes for either the creation of QoS guaranteed circuits, or the enforcement of special QoS treatments for specific packets. The selection of bearers with the appropriate characteristics constitutes the basis for the UMTS QoS provision. Each UMTS bearer is characterized by a number of quality and performance factors. The most important factor is the bearers Traffic Class; four traffic classes have been defined in the scope of the UMTS framework (i.e., Conversational, Streaming, Interactive and Background). The appropriate mapping of UMTS traffic classes to the aforementioned DiffServ service classes could offer a vehicle for the end-to-end QoS provision over a heterogeneous DiffServ/UMTS network. In this chapter, I evaluate three different mapping approaches of traffic classes for the end-to-end QoS provision over a heterogeneous DiffServ/UMTS network [7][8][9].

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 [10] and the scalable extension of H.264/MPEG-4 AVC [11][12].

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 [13]. In order to support fine-granular SNR scalability, progressive refinement (PR) slices have been introduced in the scalable extension of H.264[14]. 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.

To address the end-to-end QoS problem scalable video streaming traffic delivery over a heterogeneous IP/UMTS network, the paper proposes and validates through a number of NS2-based simulation scenarios a architecture that explores the joint use of packet prioritization and scalable video coding together with the appropriate mapping of UMTS traffic classes to the DiffServ traffic classes.

This work extends previous authors works [15] [16] taking into considerations the case of H.264/MPEG-4 AVC video streaming delivery over IP/UMTS networks.

The second case gives more complete view of the scalable video streaming over

IP/UMTS networking environments for various DiffServ/UMTS classes coupling.

The rest of the paper is organized as follows. In Section 4.2, the proposed scalable video coding techniques and prioritization framework for providing QoS guarantees for scalable video streaming traffic delivery over a heterogeneous IP/UMTS network is presented. In Section ??, I demonstrate how video-streaming applications can benefit from the use of the proposed architecture. Finally, Section 4.5 draws the conclusions of this work.

4.2 Overview of the Proposed Arrhitecture



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