Cross-Layer Adaptation for Optimized B3G Service Provisioning

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Cross-Layer Adaptation for Optimized B3G

Service Provisioning

Wolfgang Kellerer

Future Networking Lab

DoCoMo Communications

Laboratories Europe GmbH,

Munich, Germany

kellerer@docomolab-euro.com

Lai-U Choi

Media Technology Group

Institute of Communication Networks

Technische Universität München

Munich, Germany

laiuchoi@lkn.ei.tum.de

Eckehard Steinbach

Media Technology Group

Institute of Communication Networks

Technische Universität München

Munich, Germany

steinb@lkn.ei.tum.de

Abstract

Cross-Layer Adaptation (CLA) is regarded as a new paradigm for next generation wireless networks. It addresses the problems resulting from the highly dynamic characteristics of application requirements and transmission capabilities in the wireless system. In this paper, we show an implementation concept for cross-layer adaptation that is based on open programmable networks and demonstrate the potential of the CLA paradigm with recent research results on cross-layer optimized wireless video streaming.

1. Introduction

In a mobile and wireless communication environment it is still very challenging to provide reliable high-quality services due to the dynamic behavior of the wireless link. System designers have to cope with a non-predictable variation of transmission quality resulting from changing resource availability, fading errors, outages or handover. For wireless networks beyond the third generation (B3G), this dynamic behavior will be even worse since B3G is expected to span across heterogeneous wireless access network technologies with different transmission characteristics. One key factor for the success of next generation wireless networks will be to provide reliable and transparent services to the customers that make seamless use of the network diversity.

Service and application provisioning in B3G wireless networking not only has to regard network diversity but also application diversity, as new business models are expected to allow third party providers to offer their applications on top of the operators' service platforms making use of advanced open interfaces. The operators have to react to dynamically changing application requirements resulting, e.g., from changing user preferences or varying user context. To benefit from the Internet service provisioning concepts, the Internet protocol stack will be used as the basic platform for B3G systems and applications.

Cross-layer adaptation addresses the optimization of B3G systems by jointly considering several layers of the protocol stack spanning from application parameters to physical transmission. Up to now, the challenges of wireless transmission have been addressed by vertical optimization of

systems for one specific application. In layered systems such as wireless Internet certain layers have been independently optimized for worst case conditions resulting in inefficient use of available resources.

In this paper we discuss our view of the cross-layer adaptation paradigm and illustrate its advantages with a detailed example from our recent research on cross-layer optimized wireless video streaming. The remainder of this paper is structured as follows. First we give our point of view of cross-layer adaptation in Section 2 and briefly sketch an implementation concept based on open programmable networks in Section 3. After a detailed discussion of the state of the art in Section 4, we consider cross-layer optimized video streaming as an example and demonstrate the potential benefits of cross-layer adaptation by simulation in Section 5. Finally, we conclude this paper in Section 6.

2. Cross-Layer Adaptation

Cross-layer adaptation (CLA) is based on inter-layer information exchange across the traditional layers of the protocol stack. It jointly adapts all system parts to a dynamically changing environment and dynamically varying application requirements. Typically, the information travels in both directions, up and down the protocol stack as illustrated in Fig. 1. Cross-layer information exchange means for instance that the application layer receives information from lower layers about current network conditions (e.g., error probability, transmission data rate) and predictable events that influence the transmission quality (e.g., handover). On the other hand, for instance, the lower layers receive information about the current transmission requirements of the application.

The overall goal of cross-layer adaptation is to maximize the user-perceived service quality while efficiently using the available wireless resources. Thus the network operator could gain a better user satisfaction and an increased service acceptance, which is the major goal for customer-centric service provisioning. Moreover, a better utilization of the network resources would also lead to cost savings or increased revenue as, e.g., more users could be served simultaneously.

For cross-layer adaptation, not only technical system parameters such as data rate or number of users, but also meta

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information such as context information or user preferences have to be taken into account for the decision making in a joint optimization. The latter will get increasing importance for next generation personalized and context-aware wireless communication systems. Thus we assume a major part of the cross-layer adaptation control intelligence to reside in next generation service provisioning platforms [1].

Sender / Basestation Receiver

Link-Layer (MAC+PHY) Network layer Transport layer Application Link-Layer (MAC+PHY) IP TCP / UDP IP wireless channel End-to-End Optimization RTCPover TCP TCP or UDP RTPover UDP Application Bottom-up information delivery e.g., current transmission conditions Top-down information delivery e.g., QoS requirements X-layer information exchange

Sender / Basestation Receiver

Link-Layer (MAC+PHY) Network layer Transport layer Application Link-Layer (MAC+PHY) IP TCP / UDP IP wireless channel End-to-End Optimization RTCPover TCP TCP or UDP RTPover UDP Application Bottom-up information delivery e.g., current transmission conditions Top-down information delivery e.g., QoS requirements X-layer information exchange

Fig. 1. Cross-Layer Adaptation System Overview

3. Implementation Aspects:

Open Programmable System

Since current system architectures are not designed to support cross-layer adaptation, innovative concepts are required to allow the introduction of cross-layer information exchange. In [2] we propose an open programmable communication system for the implementation of this concept. Programmable platforms exist on every system layer hosting the respective functionality as platform components. Therefore, each platform consists of a stable and minimal platform base that allows coordinated configuration and additional platform components that could be added or removed (see Fig. 2).

Platform Component B1 Platform Base B Platform Component B2 Platform Component A1 Platform Base A Platform Component A2 Platform Component B1 Platform Base B Platform Component B2 Platform Component A1 Platform Base A

API1 API2 API1 API2

Fig. 2. Programming Cross-Layer Interfaces

In this programmable platform architecture, new components realizing adaptation functions and associated interfaces for cross-layer communication could easily be introduced as additional components [2]. Fig. 2 illustrates the implementation of a new component to realize a cross-layer communication application programming interface (API).

4. Background and Previous Work

Research on cross-layer adaptation has just recently received attention by the research community. However, there are a number of mechanisms already existing on all layers that allow adaptation. The application for example can adapt to the varying network characteristics by adequate data processing at the application, presentation and session layers, such as dynamic rate variation in the video (or audio) encoder [3] or decoder [4], joint application source rate and forward error

correction code rate adaptation [5] [6], and multiple description encoding [7]. In case that the application layer receives timely information of the transmission status of each media unit, rate-distortion optimized transmission of media data (or frame scheduling) [8] can be employed to optimally adapt to changes in transmission quality. On the other hand, the link and network can adapt to the application’s QoS requirements by processing at the physical, data link and network layers, such as multi-user scheduling or resource allocation [9] controlled at the data link layer or medium access control (MAC) layer, and signal processing at the physical (PHY) layer, such as adaptive modulation and channel code rate (e.g., HSDPA, GPRS, IEEE802.11a, HiperLAN2) and adaptive number of data streams or users in smart antenna array systems [10].

Previous work mainly concentrates on optimizing the performance at a single layer, such as the adaptation of the application to the transport, network, data-link and physical layer characteristics (bottom-up approach) and the adaptation of the physical, data link or network layers to the application requirements (top-down approach).

Most of the on-going research in cross-layer optimization focuses on joint optimization of the physical layer and data link (or MAC) layer (e.g., [11]-[17]). [11] and [12] attempt to provide an overview of the cross-layer paradigm shift that is beginning to take place as wireless communication evolves from a circuit-switched infrastructure to a packet-based infrastructure, in which cross-layer is mainly referring to the data link layer and physical layer. In [13], the multi-user scheduling (or queuing) method, Join-the-Shortest-Queue (JSQ) prefetching protocol (allocated at data link layer), is evaluated in a wireless CDMA system for the downlink streaming of prerecorded video with prefetching. The prediction of the multiple access interference (MAI) (allocated at physical layer) is integrated for the scheduling. In [14], joint physical-MAC layer optimization in OFDM systems using adaptive modulation and subcarrier allocation is investigated based on utility theory. It is based on maximizing the sum of the utilities over all active users in time-varying wireless environments. On the other hand, [15] provides some investigations of different error control and adaptation mechanisms in IEEE802.11 standard MAC layer and IEEE802.11a standard physical layer. The MPEG-4 FGS hierarchal coding scheme is employed in the simulation. [16] and [17] give a dynamic programming formulation for computing optimal transmit power control and transmission data rate control. Delay constrained traffic is considered in a TDMA system and the objective is to minimize the transmit power.

Some on-going research considers the optimization of routing at the network layer in the context of cross-layer optimization for ad hoc wireless networks (e.g., [18], [19]). In [18], a methodology for studying the performance of wireless ad hoc networks with multihop routing is introduced. Some performance investigation for the interaction of power control (allocated at physical layer), queuing discipline (allocated at MAC layer), and the choice of routing (allocated at network layer) are provided. In contrast, [19] considers an integration of joint middleware and routing. One could categorize this work as cross-layer design between application layer and network layer. Other on-going research includes the source rate in the joint optimization of transmit power and forward error correcting coding at the physical layer (e.g., [20]). In [20], adaptive joint source coding and power control in conjunction with joint source-channel coding is proposed for transportation of digital

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video over CDMA cellular networks. The joint source and channel coding and decoding is studied from the information point of view.

Besides, a cross-layer outline for multimedia delivery over wireless Internet is presented in [21]. In particular, it considers three key components in wireless Internet infrastructure (i.e., multimedia server, base station or gateway, and mobile hosts) and provides some discussion of the functionalities for the cross-layer architecture. Similar concepts of cross-layer optimization can be applied to a laptop system and [22] studies such a cross-layer joint adaptation among application, operational system (OS), and hardware (e.g., CPU frequency) on a laptop system.

5. Cross-Layer Optimization of Wireless Video

Streaming

To demonstrate the potential benefits of the cross-layer adaptation paradigm, we take wireless video streaming as an example scenario. Already with a simple scenario we are able to show the advantages of cross-layer design when compared to a traditional system design. In the following, we consider a video streaming server and streaming clients residing in mobile devices. For simplicity, we assume the streaming server to collocate with the base station. Our goal is to maximize the end-to-end quality perceived by the users while efficiently using the wireless resources. We focus on the video streaming at the application layer and multi-user transmission at the link layer (MAC layer and PHY layer).

5.1 Video Streaming

In the following, we briefly discuss the main features of streaming video that are relevant in our scenario. The video is encoded using a standard video compression scheme (e.g., H.264), and the corresponding video stream is stored on the streaming server. When the stream is requested by the client, it is packetized and sent to the user. The receiver at the user pre-buffers some data before playout begins which allows us to smooth some of the transmission quality variation and to deal with delay jitter. The end-to-end latency of the application is directly related to the amount of data that is stored prior to playout. The number of bits to be sent for each video frame depends on what kind of encoding mode has been selected. We distinguish so called I-frames that are encoded without reference to previous frames and P-frames that are encoded by forming a prediction from previous frames. While I-frames can be decoded without receiving the previous frame, P-frames typically can not. As a result, I-frames are bigger than P-frames. In order to allow fast forward and interactive scene selection, frames are typically introduced about every 500-1000 ms. An I-frame and all the following P-I-frames up to and excluding the next I-frame will be referred to in the following as a group of pictures (GOP).

The reconstruction quality at the receiver depends on the number of successfully decoded frames within a group of pictures. Since successful decoding of P-frames depends on error-free reception of all previous frames of the same group of pictures, losing the first frame of a GOP leads to the worst result. In this case, the most recent decoded frame is displayed as a still image until the next I-frame is successfully received. Losing the last frame of a GOP leads to little distortion as just the second last frame of the GOP is displayed twice. Fig. 3 shows the mean squared reconstruction error (MSE) for a group of pictures consisting of 15 frames when losing different frames

for 3 different videos, namely Carphone, Foreman and Mother-daughter. 1 2 4 6 8 10 12 14 16 0 100 200 300 400 500 600 700 800 900 Index MSE Carphone Foreman Mother−daughter

Fig. 3. MSE for a group of picture in 3 videos

As can be seen from Fig. 3, the MSE is largest when losing the first frame of a GOP. The distortion decreases as we proceed within the GOP and becomes just the encoding distortion, which is a function of the bit-rate of the video stream, when we receive all frames in time and error-free (referred to as index 16 in Fig. 3). The actual error depends on the scene content. If there is little motion in the sequence, the loss of a frame has little effect on the quality of the reconstructed sequence. If there is significant motion, however, the influence of a lost frame can be dramatic.

In video data scheduling, the application has to decide when to send a frame [8]. As the first frame of a GOP is the most important one, the application will increase the probability of error-free reception by prioritizing this frame during scheduling. This could mean for instance, that the I-frame is transmitted twice while all other frames of the GOP are transmitted just once. Depending on the available transmission rate, the application therefore can choose between different frame scheduling patterns.

5.2 Multi-User Transmission

In wireless networks, each base station serves multiple users (clients) or mobile stations by means of time division, frequency division, or code division. Multi-user transmission scheduling determines for instance which user is allowed to use the channel at a given time, frequency or code. By smartly scheduling the multi-user transmission based on user demand and channel state, the efficiency of utilizing the resource can be improved significantly. With different scheduling or arrangement of the transmission, each user might obtain different transmission data rates. Table 1 gives an example of 3 users (A, B, and C) with 7 different cases (Case I to Case VII), in which each user is arranged with a particular ratio of time for transmission. If a user’s transmission data rate is assumed to be equal to 100 kbps when 2/9 of the total transmission time is assigned for it, its transmission data rate will becomes 150 kbps and 200 kbps when 3/9 and 4/9 of the total transmission time are assigned to it, respectively.

Table 1. Different cases of transmission time arrangement Case I II III IV V VI VII

A 3/9 4/9 4/9 3/9 2/9 3/9 2/9 B 3/9 3/9 2/9 4/9 4/9 2/9 3/9 C 3/9 2/9 3/9 2/9 3/9 4/9 4/9

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5.3 Cross-Layer Optimization

To carry out the cross-layer optimization, information has to be abstracted from different layers and the optimization has to be done centrally. We propose a cross-layer optimizer as shown in Fig. 4 to provide the end-to-end optimization. This figure illustrates the tasks of the optimizer. Status information is collected from the selected layers, the application layer and the link layer (consisting of MAC layer and PHY layer). Some necessary information is abstracted from each layer for every user. The optimizer makes the decision according to a particular objective function. After the decision is made, the optimizer distributes the decision information back to the corresponding layers. We would like to emphasize that the objective function at the optimizer should be application-oriented because the objective is to maximize the end user perceived quality. Also, note that since there may be an infinite number of possible combinations of the parameters, this architecture includes an abstraction of finite parameter combinations in order to obtain the optimization quickly. Therefore, it should be noted that the final decision might be a local optimum since only a subset of the combinations are analyzed.

Link-Layer (MAC+PHY) Network layer Transport layer Application X-layer Optimizer (application-oriented) Decision Parameter Abstraction Parameter Abstraction Decision Distribution Decision Distribution Link-Layer (MAC+PHY) Network layer Transport layer Application X-layer Optimizer (application-oriented) Decision Parameter Abstraction Parameter Abstraction Decision Distribution Decision Distribution

Fig. 4. Configuration of the proposed cross-layer optimizer

5.4 Simulation Results

We provide a selected example to evaluate the proposed architecture and underline the potential of cross layer information exchange. In this example, 3 users with 3 different videos (as shown in Fig. 1) are optimized with 7 different cases from multi-user scheduling (as shown in Table 1), in which each user is arranged with a particular ratio of time for transmission. We assume that user A, B and C request the

Carphone, Foreman, and Mother-daughter video, respectively. In the simulation, the source rate of all the three videos is 100 kbps and we assume the size of the first frame, which is an I-frame, is 4 times the size of the other 14 frames (which are P-frames) in the group. We also assume the packet size is equal to a P-frame. The channel coherent time is assumed to be 50 ms, which approximately corresponds to pedestrian speed. A user’s transmission data rate is assumed to be equal to the source data rate when 2/9 of the total transmission time is assigned for it. When more transmission time is assigned for a user, the transmission data rate is larger than the source data rate and the video is retransmitted from the first frame to fulfill the rate. This means that more important frames are retransmitted. We assume no acknowledgement (ARQ) is available in this system. One case out of the 7 cases is chosen by maximizing the minimum performance among the 3 users. Here, we choose the peak-signal-to-noise ratio (PSNR) as our performance measure. PSNR is defined as PSNR=10

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log10(2552/MSE). The larger the PSNR is, the smaller the MSE, which is a measure of distortion. The MSE is compared between the original video sequence and the reconstructed sequence at the client. Therefore, the larger the PSNR is, the better the performance. The optimization proceeds by computing the expected MSE for every GOP for

every user in each case in Table 1. The case that minimizes the MSE for the worst quality user is selected as the best case for transmission. This selected case is signaled to the link layer in order to control the transmission.

Fig. 5 shows the PSNR improvement of the worst user in the selected case compared to that in case 1, which can be considered as the performance without cross layer optimization. The x-axis is ∆PSNR, which is defined as ∆PSNR= (PSNR of the selected case) – (PSNR of case 1). We can see from the figure that there is a 40% chance that the improvement is larger than 1 dB, which is fairly significant.

0 0.5 1 1.5 2 2.5 3

10−2

10−1

100

∆ PSNR compared to Case 1 (in dB)

Cumulative density probability function (CDF)

1000 samples, 3 users w/ 7 cases, 3 videos w/ 100kbps

Fig. 5. Performance improvement of 3 users with 7 cases of transmission time arrangement

The improvement in Fig. 5 can be explained by Fig. 6, in which the histogram of the selected cases is provided. There is more than 42% of the chance that the selected case is Case IV. The reason is related to the requested videos by the users. The MSE when losing a frame in the Foreman video, which is requested by user B, is relatively large compared to others. Therefore, user B is more sensitive to channel error, which in turn requires more transmission data rate (200 kbps or 4/9 of the time) in order to transmit more reliably. One interesting observation during the simulation is that the selected case is Case IV sometimes even when the channel conditions of the three users are the same. This is because our objective function is application-oriented.

Finally, we would like to point out that in this example only 7 cases (or combinations) are abstracted from the link layer. When more cases with significant impact are abstracted for the optimizer, the amount of improvement will increase. However, the computational complexity of the optimizer will also increase.

6. Conclusions

In this paper we have discussed the concept of cross-layer adaptation and we have demonstrated the potentials of this paradigm with recent research results on cross-layer optimized wireless video streaming.

As we have seen from a detailed analysis of the state of the art, most projects are concentrating on joint channel and source coding, joint optimization of physical and medium access layers, or work in the area of ad hoc networks. There is only little work on the joint optimization of application and link layers, including the physical and medium access layers, as we are discussing in this paper.

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Our analysis shows, that already in simple scenarios significant improvements could be achieved by cross layer adaptation. By the joint optimization of several system layers, we not only could optimize the existing resources statically, but also could react to the changing system environments..

To work towards a general framework for cross-layer optimization, our next steps include the consideration of acknowledgements and adaptive modulation and coding (AMC) at the link layer. Furthermore, we will include the network layer into our considerations.

Selected Case

Frequency

1000 samples, 3 users w/ 7 cases, 3 videos w/100kbps

I II III IV V VI VII 0 50 100 150 200 250 300 350 400 450

Fig. 6. Histogram of the selected case for the example (3 users with 7 cases of transmission time arrangement)

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