LTE TECHNOLOGY UPDATE


Quality-Driven Cross-Layer
Optimized Video Delivery over LTE
Haiyan Luo, University of Nebraska-Li...
user with the best instantaneous channel quality.
The main problem of this scheduling is its inher-
ent unfairness and cov...
In the proposed                                                                Time

  WRR-based radio
                   ...
To achieve higher
                  Video                                                                                 ...
With AMC, the                                                             No more resource block, iteration ends
         ...
rb1    rb2       rb3      rb4     rb5    rb6     rb7       rb8      rb9         rb10     rb11       rb12      rb13        ...
pixels in the current frame. According to the
              48
                                                           ...
CONCLUSIONS                                   ferences such as IEEE INFOCOM, IEEE GLOBECOM, and
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Cm 2010 Quality Driven Cross Layer Optimized Video Delivery Over Lte

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Cm 2010 Quality Driven Cross Layer Optimized Video Delivery Over Lte

  1. 1. LTE TECHNOLOGY UPDATE Quality-Driven Cross-Layer Optimized Video Delivery over LTE Haiyan Luo, University of Nebraska-Lincoln Song Ci, University of Nebraska-Lincoln and Chinese Academy of Sciences Dalei Wu, University of Nebraska-Lincoln Jianjun Wu, Huawei Technologies Hui Tang, Chinese Academy of Sciences ABSTRACT tiveness in a longer time frame. LTE improves the Universal Mobile Telecommunications Sys- 3GPP Long Term Evolution is one of the tem (UMTS) mobile phone standard and pro- major steps in mobile communication to enhance vides a greatly enhanced user experience for the the user experience for next-generation mobile next generation mobile broadband. Figure 1 broadband networks. In LTE, orthogonal fre- illustrates the overall architecture of real-time quency-division multiple access is adopted in the video delivery over LTE cellular networks, in downlink of its E-UTRA air interface. Although which the network is comprised of the access cross-layer techniques have been widely adopted network and the core network, known as evolved in literature for dynamic resource allocation to UMTS terrestrial radio access network (E- maximize data rate in OFDMA wireless net- UTRAN) and evolved packet core (EPC), works, application-oriented quality of service for respectively. video delivery, such as delay constraint and The evolved UMTS terrestrial radio access video distortion, have been largely ignored. (E-UTRA) system of LTE uses orthogonal fre- However, for wireless video delivery in LTE, quency-division multiple access (OFDMA) for especially delay-bounded real-time video stream- the downlink and single-carrier FDMA (SC- ing, higher data rate could lead to higher packet FDMA) for the uplink. The downlink transmis- loss rate, thus degrading the user-perceived sion scheme of frequency-division duplex video quality. In this article we present a new (FDD) and time-division duplex (TDD) modes QoS-aware LTE OFDMA scheduling algorithm is based on conventional orthogonal frequency- for wireless real-time video delivery over the division multiplexing (OFDM). The available downlink of LTE cellular networks to achieve spectrum is divided into multiple resource the best user-perceived video quality under the blocks based on the time and frequency given application delay constraint. In the pro- domains. A resource block is the smallest allo- posed approach, system throughput, application cation unit in LTE OFDM radio resource QoS constraints, and scheduling fairness are scheduling, which can be independently modu- jointly integrated into a cross-layer design frame- lated by a low-rate data stream. As compared work to dynamically perform radio resource allo- to OFDM, OFDMA allows multiple users to cation for multiple users, and to effectively access the available bandwidth and assigns spe- choose the optimal system parameters such as cific time-frequency resources to each user; modulation and coding scheme and video encod- thus, the data channels are shared by multiple ing parameters to adapt to the varying channel users. Therefore, higher efficiency can be quality of each resource block. Experimental achieved at the expense of increased complexity results have shown significant performance in terms of resource scheduling [1]. enhancement of the proposed system. Due to the scarcity of radio resources, it is of vital importance for the LTE wireless communi- INTRODUCTION cation system to make efficient usage of the available radio resources. To achieve this, the The increasing popularity of mobile data and main task of an OFDMA scheduler is to allocate emerging video applications such as multimedia radio resources intelligently so that higher sys- online gaming (MMOG), mobile TV, and video tem throughput can be obtained. In the litera- streaming services have motivated the Third ture different OFDMA scheduling algorithms Generation Partnership Project (3GPP) organi- have been proposed. For example, maximum zation to work on the standard of Long Term carrier-to-interference (C/I) scheduling, which Evolution (LTE), one of the major steps in essentially ranks all users according to their mobile radio communication to ensure competi- instantaneous C/I ratios, always schedules the 102 0163-6804/10/$25.00 © 2010 IEEE IEEE Communications Magazine • February 2010
  2. 2. user with the best instantaneous channel quality. The main problem of this scheduling is its inher- ent unfairness and coverage limitation. In Round-Robin, another popular scheduling algo- Uu rithm, users are served in a round-robin manner. Media server The user that has not been served for the longest Uu time is selected; thus, the instantaneous channel X2 eNodeB Uu quality is not considered. Hence, although it S1 Evolved packet ensures fairness, it may suffer from low through- Internet eNodeB core (EPC) X2 put. In addition, in the Proportional Fairness (PF) scheduling algorithm the system can X2 achieve long-term fairness but cannot guarantee the delay constraint for real-time video services [2]. Therefore, these scheduling methods cannot achieve satisfactory system performance for real- eNodeB time video delivery over LTE cellular networks. Over the past several years, some research work Evolved UTRAN (E-UTRAN) has been done to improve the OFDMA schedul- ing performance in LTE cellular networks. For Figure 1. The overall architecture of real-time video delivery over LTE cellular example, Kwan et al. in [3] proposed to formu- networks. late the scheduling problem so as to jointly maxi- mize the sum of the bit rates for all users and provided a suboptimal solution by simplifying as follows. We first introduce an overview of the problem into several parallel single-user the proposed quality-driven cross-layer system, optimization problems. In [4] the authors pro- followed by the proposed OFDMA dynamic posed and investigated a multicarrier gradient resource allocation. We then present the mod- scheduling algorithm with minimum/maximum ulation and coding design, and the application rate constraints for the downlink shared data layer encoder behavior and design metric. Fur- channel (SDCH). thermore, we discuss the formulation and solu- Thus, although OFDMA scheduling has been tion of the proposed system. The details of widely studied, the common practice is to maxi- experimental study and analysis are then mize the data rate under the total power con- described. The final section concludes this straint, while the application design metrics are article. usually ignored. Furthermore, there has been lit- tle work done on the LTE scheduling issue, especially for video delivery over LTE. However, SYSTEM OVERVIEW application quality of service (QoS) constraints In this work we consider a single-cell downlink such as video quality and packet playback con- scenario for an LTE cellular system using adap- straints are very important performance metrics tive modulation and coding (AMC). Transmis- that should be considered in the LTE OFDMA sion power is assumed to be uniformly scheduling for real-time wireless video delivery. distributed among all subcarriers. This assump- To explain, higher system throughput does not tion is reasonable due to the fact that when necessarily mean higher video quality at the AMC is used, the system performance improve- receiver’s end. Actually, with the increase of sys- ment is negligible even if a different power level tem throughput, the average packet loss rate will is allocated to each subcarrier, thanks to statisti- also increase, which could degrade the overall cal effects [5]. OFDMA is the multicarrier mod- user-perceived system performance in terms of ulation technique that has been adopted in the video quality. downlink of LTE cellular networks. As shown in Aiming at solving this problem, in this article Fig. 2, the radio frame of LTE OFDMA is divid- we present a new OFDMA scheduling algorithm ed into 20 0.5 ms slots in the time domain. Each for real-time video delivery over LTE cellular time slot normally carries seven symbols. As the networks. A Weighted Round-Robin (WRR) basic time-frequency unit in the scheduler, a algorithm is proposed to achieve high system resource block consists of two time slots in the throughput, guarantee application layer QoS time domain, with 12 adjacent subcarriers in the such as delay constraint, and ensure fairness. frequency domain. In this article we consider a Furthermore, to achieve the best user-perceived system of N users and K subchannels for video quality under the given QoS delay con- OFDMA scheduling of LTE video delivery, as straint, we propose a cross-layer optimized sys- shown in Fig. 2. According to the LTE standard, tem to dynamically determine moculation and each resource block can only be assigned to one coding scheme (MCS) and codec parameters user within a given cell. Also, all the downlink such as quantization step size (QP) based on the resource blocks that are assigned to a single user radio resource allocation result and the instanta- can only adopt one MCS within each scheduling neous channel conditions of resource blocks. period [6]. Here, a scheduling period is defined Also, the optimal system parameters achieved as the time duration needed to allocate the radio from the cross-layer design framework are uti- resources of one OFDM frame. Additionally, lized as well to improve the result of dynamic within each resource block, the quality of the radio allocation. The effectiveness of the pro- subcarriers may differ due to frequency selectivi- posed system is verified by using the H.264 ty fading. The vector of the resource block rb, audio-video codec (AVC). which provides a good collective representation The remainder of this article is organized of the quality of all the subcarriers within the IEEE Communications Magazine • February 2010 103
  3. 3. In the proposed Time WRR-based radio U1 resource allocation S1 algorithm, single or S2 U2 multiple resource blocks can be U3 assigned to one user Frequency in a resource U4 allocation period. We assume all users in the system are real-time video users. Sk-1 UN Sk Resource block Slot Figure 2. Time-frequency OFDMA radio resources with multiple users in LTE cellular networks. resource block, is reported back by user n via a In general, the user-perceived video quality, feedback channel. which is quantified by expected video distortion Thus, the main issue to be addressed in this in this article, can be jointly minimized by coor- article is to dynamically assign all the available dinating encoding parameters and packet loss radio resources to the given N users, and encode rate (PLR). PLR is then collectively affected by and transmit the video data through the down- the varying channel quality of resource blocks link SDCH with optimal MCS and video codec and the adopted MCS on the resource blocks. parameters. Therefore, the best user-perceived Larger QP and smaller PLR can improve video video quality for every delay-bounded wireless quality, but finer encoding parameters of larger video delivery can be achieved by adapting to QP increases data traffic accordingly. Further- the instantaneous channel conditions. more, higher data rates from LTE OFDMA We develop a cross-layer optimized video scheduling can lead to higher PLR but may delivery system over LTE cellular networks, cause video quality degradation, while lower consisting of modules of video application, data rates can provide higher transmission qual- cross-layer optimization, dynamic resource allo- ity at the cost of QoS guarantees [7]. Thus, all cation, and wireless delivery, as shown in Fig. 3. these network functions should be systemically In the module of dynamic resource allocation, considered toward the optimal trade-off to overcome distortion degradation and between the maximization of system throughput throughput reduction problems while guaran- and the optimization of user-perceived video teeing fairness among all users, we propose an quality. The proposed cross-layer optimized sys- algorithm to perform dynamic radio resource tem is able to effectively achieve this by holisti- allocation based on WRR to jointly consider cally integrating all the key system parameters three major factors: pre-known packet delay into a distortion-delay optimization framework constraint, varying channel quality, and histori- to jointly determine the best system parameters cal average data rate of user n on a given under the given resource and QoS constraints resource block. In the video application module for real-time video delivery over LTE cellular the system can perform video encoding by networks. dynamically adapting to the channel quality of any resource block fed back from the module of wireless delivery. The cross-layer optimiza- DYNAMIC RESOURCE ALLOCATION tion module performs cross-layer optimization In order to exploit multi-user diversity and to determine the best MCS and encoder param- increase resource allocation flexibility, OFDMA eters based on the radio resource allocation is employed in LTE cellular networks to allow outcomes and the video application characteris- multiple users to share subcarriers simultaneous- tics to achieve the best user-perceived video ly. In the proposed WRR-based radio resource quality under the given QoS constraint of an allocation algorithm, single or multiple resource end user. The selected MCS for user n at blocks can be assigned to one user in a resource resource block k determines the instantaneous allocation period. We assume all users in the data rate and the historical average data rate. system are real-time video users. This is reason- The latter is then utilized by the resource allo- able since it can easily be extended to systems cation module to avoid certain users holding with non-video users by incorporating the class resources for too long. of service (CoS) concept. 104 IEEE Communications Magazine • February 2010
  4. 4. To achieve higher Video overall system sequence throughput, the larger is the available Video encoding channel rate for a Video application given user on a Channel quality feedback certain resource Estimate distortion block, the higher is Optimized the need to assign Cross-layer strategy Delay Wireless delivery this resource block constraint optimization to the user. This Historical data rate factor is called a Resource allocation result positive factor. Dynamic resource allocation (DRA) Channel quality feedback Figure 3. The system overview of the proposed quality-driven cross-layer optimized video delivery over LTE cellular networks. To jointly consider system throughput, appli- vector will not be affected by channel conditions. cation QoS constraints, and scheduling fairness, Meanwhile, a smaller delay constraint means the presented OFDMA resource allocation higher urgency for the current user to use radio algorithm jointly considers three design factors resources for transmission so that the playback for each user: available channel rate of a user deadline can be met and higher video quality on the given resource block, application video can be achieved. Therefore, this factor is called a packet delay constraint, and historical average negative factor. data rate of each user on the given resource Furthermore, to avoid those users within block. Each design factor is achieved by weight- close proximity to eNodeB occupying radio ing the user against all the available resource resources all the time, a third design factor blocks in the current OFDM frame. Denote the needs to be incorporated to ensure the fairness total available resource blocks of a given OFDM of resource allocation. The historically average tot frame N RB ; then each design factor is a vector data rate of a user, denoted wc, is defined as the with K elements. For each user, adding the average data rate of this user on a given resource three design factors together, we can achieve an block within a given time window tw. A smaller overall decision weight vector, also consisting of value of tw indicates that the system is greedier K elements. to achieve the maximization of system through- In a resource allocation period, each resource put at the expense of resource allocation fair- block is associated with different channel quality ness, while a wider time window means that in terms of signal-to-noise ratio (SNR), which is more weight is given to allocation fairness than piggybacked by the previous user of this to overall throughput. The vector elements are resource block. Given a certain user, the avail- normalized against the maximum historical aver- able channel rate on each resource block can be age data rate of all waiting users on all resource evaluated according to the Shannon-Hartley blocks. A greater value of this vector element theorem. We denote this design factor wa. The means less priority for allocating the current elements of wa are normalized against the maxi- resource block to the given user, meaning that mum available channel rate of all users on all this is also a negative factor. resource blocks. Thus, to achieve higher overall With these three design factors, user n is system throughput, the larger the available jointly weighted on all available resource blocks channel rate for a given user on a certain of a given OFDM frame. In addition, all three resource block, the higher the need to assign design factors are constantly updated after the this resource block to the user. This factor is transmission of each OFDM frame. Then the called a positive factor. overall decision weight vector for any user n Each user n is also associated with a QoS can be represented as w n = {w n,rb,0 < ∀n ≤ N tot max delay constraint Tn at any resource allocation and 0 < ∀rb ≤ N RB } = α a w a – α b w b – α c w c , n n n n n n period. Applying the application delay constraint where α a , α b , and α n are the linear factors of n n c of each user to all resource blocks, we can user n that decide the relative significance of achieve the second design factor, denoted w b . these three design factors. Therefore, a deci- The elements of w b are normalized against the sion weight vector is achieved for each user on maximum delay constraint of all waiting users. all resource blocks to perform dynamic resource Thus, for each user, the element values are the allocation. same for all resource blocks, meaning that this In the proposed WRR-based dynamic IEEE Communications Magazine • February 2010 105
  5. 5. With AMC, the No more resource block, iteration ends Start of a new combination of OFDM frame different constella- tions of modulation and different rates of Build design vector wna based on fed-back channel qualities For the first error-control codes element in the are chosen by combined vector adapting to the Build design vector wnb based on QoS time-varying channel constraints of user applications quality of the assigned resource Allocate the resource Build design vector wnc based on block to the current user blocks for every user. historical average data rate Estimate the overall weight vector for Delete all elements relating to every user n: wn=αnawna-αnbwnb-αncwnc the current resource block from the combined vector Combine the weight vectors for all users in descending order Figure 4. The WRR-based dynamic resource allocation algorithm in the proposed quality-driven cross-layer optimized video delivery over LTE cellular networks. resource allocation algorithm, within a resource MODULATION AND CODING allocation period, the three design factors are first calculated for each user according to the In LTE, AMC is employed to improve the spec- current user status, channel conditions and his- tral efficiency of subchannels. Thus, the bit error torical average data rates. Then the overall e rate pm is decided by the dynamically selected weight wn for each user n can be achieved. If the scheme of AMC, which has been adopted to waiting queue of a user is empty, this user is not enhance the throughput of future wireless com- considered for resource allocation. Afterward, munication systems. With AMC, the combina- the elements of all N user decision vectors are tion of different constellations of modulation combined and sorted into one vector in descend- and different rates of error control codes are ing order. Based on this order, the resource chosen by adapting to the time-varying channel block is assigned to the user with the highest quality of the assigned resource blocks for every weight, and all elements regarding this resource user. For example, in better channel conditions, are then deleted from the overall vector to avoid an AMC scheme with larger constellation sizes duplicate allocation. If two or more waiting users and higher channel coding rate can be used to have the same weight, they will be allocated guarantee the required performance on packet resource blocks in a round-robin way. This pro- error rate, meaning that AMC can effectively cess continues until all the resource blocks of the decrease the transmission delay while satisfying current OFDM frame are allocated. Then the the constraint of PLR. On the contrary, to algorithm proceeds to allocate resources for the ensure the application video quality for end next OFDM frame. The flow chart of the algo- users, smaller constellation sizes and lower chan- rithm is illustrated in Fig. 4. nel coding rate should be adopted when the Figure 5 exemplifies how the proposed cross- wireless channel experiences bad conditions. layer-based OFDMA resource allocation works According to the LTE standard, quaternary in a scheduling period, where two subchannels phase shift keying (QPSK), 16-quadrature ampli- and two users (U1 and U2) are considered. Thus, tude modulation (QAM), and 64-QAM are sup- there are 20 resource blocks for each OFDM ported on the downlink DSCH. Thus, in this frame. Each user is weighted against all the article we consider the AMC schemes of QPSK available RBs through the three factors. Then 1/2, QPSK 3/4, 16-QAM 1/2, 16-QAM 3/4, and the resulting overall weight vectors w 1 and w 2 64-QAM 3/4. Furthermore, bit error rate (BER) are illustrated in Fig. 5a, and sorted together in e pm can be approximated from the received SNR descending order in Fig. 5b. The result of the [7]. Therefore, given medium access control proposed dynamic resource allocation is shown (MAC) frame size and packet length, MAC in Fig. 5c, where each grid represents a resource frame error rate (FER) and PLR can be calcu- block in the illustrated OFDM frame. lated from the BER approximation [7]. 106 IEEE Communications Magazine • February 2010
  6. 6. rb1 rb2 rb3 rb4 rb5 rb6 rb7 rb8 rb9 rb10 rb11 rb12 rb13 rb14 rb15 rb16 rb17 rb18 rb19 rb20 w1 0.2 0.1 -0.2 0 -0.1 0.3 -0.2 0.5 0.6 -0.1 0 0.4 -0.5 0.7 0.2 -0.2 0 0.3 0.2 0.5 w3 -0.1 0 -0.2 0.3 0.5 -0.2 -0.4 0.2 0.4 -0.1 0.3 -0.2 -0.5 0.5 0.2 -0.2 -0.1 0.2 0 0.1 (a) Resource block rb14 sb9 rb5 rb8 rb14 rb20 rb9 rb12 rb4 rb6 rb11 rb18 rb8 rb1 rb15 rb15 rb19 rb18 rb20 rb2 (user) (U1) (U1) (U2) (U2) (U2) (U2) (U2) (U2) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U1) Weight 0.7 0.6 0.5 0.5 0.5 0.5 0.4 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 Resource block rb2 rb4 rb19 rb11 rb17 rb1 rb5 rb10 rb10 rb17 rb3 rb3 rb7 rb6 rb16 rb12 rb16 rb7 rb13 rb13 (user) (U2) (U1) (U2) (U1) (U1) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U1) (U2) (U2) (U2) (U1) (U2) Weight 0 0 0 0 0 -0.1 -0.1 -0.1 -0.1 -0.1 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.4 -0.5 -0.5 (b) rb1 rb2 rb3 rb4 rb5 rb6 rb7 rb8 rb9 rb10 (U1) (U1) (U1) (U2) (U2) (U1) (U1) (U1) (U1) (U2) rb11 rb12 rb13 rb14 rb15 rb16 rb17 rb18 rb19 rb20 (U2) (U1) (U1) (U1) (U2) (U1) (U1) (U1) (U1) (U1) (c) Figure 5. An illustrated example of the proposed cross-layer based OFDMA dynamic resource allocation with two users (U1 and U2) and two subchannels for wireless video delivery over LTE cellular networks: a) the calculated two weight vectors w1 and w2 for the two users U1 and U2 on all resource blocks of the current OFDM frame; b) the sorted overall decision weight vector (by descending order) for all users on all available resource blocks of the current OFDM frame; c) the result of the proposed dynamic resource allocation. VIDEO DISTORTION been advocated to provide an accurate estima- tion of video distortion [8], in which the esti- At the application layer, hybrid motion-compen- mated video distortion is determined by packet sated video coding is usually adopted for trans- loss rate and encoding parameters such as quan- mission over lossy channels. H.264 provides an tization step size QP. Therefore, in this article interface for flexible, bandwidth optimized video minimizing the estimated distortion achieved by streaming transmission in LTE networks. There- the ROPE method is adopted as the QoS design fore, it plays a critical role in the distribution of objective. digital video over LTE networks [1]. In H.264 each frame is generally represented in block-shaped units of associated luminance CROSS-LAYER OPTIMIZED SYSTEM and chrominance samples (16 × 16 pixel region) called macroblocks (MBs). MBs can be both AND THE SOLUTION intra-coded and inter-coded from samples of To improve the user-perceived video quality for previous frames. Intra-coding is performed in end users of wireless video delivery, the expected the spatial domain by referring to neighboring mean square error (MSE) between the received samples of previously coded blocks, which are to pixels and original pixels of the video frames is the left and/or above the block to be predicted. used as the distortion metric [8]. Thus, the mini- Inter-coding is performed with temporal predic- mization of expected video distortion E d (ρ), tion from samples of previous frames. Many which is accurately calculated by the ROPE coding options exist for a single macroblock, and method under instantaneous network conditions, each of them provides different rate distortion becomes the objective function in the proposed characteristics. In this work only predefined optimization framework. E d(ρ) is also affected macroblock encoding modes are considered, by the encoding parameters used. Given packet since error resilience source coding is applied by length, packet loss rate ρ can be further calculat- selecting the encoding mode of each particular e ed from BER pm. Meanwhile, transmission delay macroblock. This is crucial to allow the encoder ttrans, which is constrained by a maximum delay to trade off bit rate with error resiliency at the bound, can be represented by dividing data bit macroblock level. rate Rm by bandwidth. Rm is further determined At the application layer, video distortion is by the selected MCS on a given resource block the most direct QoS performance metric from [7]. Therefore, the proposed cross-layer opti- the perspective of end users. For real-time mized problem can be formulated as a distor- source coding, video distortion can be caused by tion-delay optimization problem, where the quantization, packet loss, error concealment, design objective is to minimize the expected and so on. In the literature the Recursive Opti- video distortion under the given packet delay mal Per-Pixel Estimate (ROPE) method has constraint. IEEE Communications Magazine • February 2010 107
  7. 7. pixels in the current frame. According to the 48 H.264 standard, one slice is coded into one packet in the experiment. Given an average SNR —, the instantaneous γ 46 link quality γ can be randomly produced from Rayleigh distribution. One subcarrier has band- 44 width of 15 kHz; thus, each resource block has a bandwidth of 180 kHz. We consider a system with 10 subchannels. Moreover, a single-hop sce- 42 nario is considered in the experiments to verify the performance of the proposed system. Similar PSNR (dB) 40 conclusions derived from the single hop scenar- ios may directly apply to multihop scenarios when the channel state information (CSI) of 38 each hop is available. Without losing generality, we assume that 36 U1: without cross-layer optimization (Tmax = 20 ms) there are three waiting users U 1 ,U 2 ,U 3 , with n U1: with cross-layer optimization (Tmax = 20 ms) n delay constraints 20 ms, 30 ms, and 40 ms, U2: without cross-layer optimization (Tmax = 30 ms) n respectively. These delay constraints are set in 34 U2: with cross-layer optimization (Tmax = 30 ms) accordance with the frame rate [10]. For cross- n U3: without cross-layer optimization (Tmax = 40 ms) n U3: with cross-layer optimization (Tmax = 40 ms) layer optimization, QP q and MCS m are consid- n 32 ered. The values of QP are allowed to select 15 20 25 30 35 from 150, while the available MCS schemes are SNR (dB) chosen from QPSK 1/2, QPSK 3/4, 16-QAM 1/2, 16-QAM 3/4, and 64-QAM 3/4. We compare the Figure 6. PSNR performance improvement verification of the proposed quality- peak SNR (PSNR) improvement of the pro- driven cross-layer optimized video delivery for three video users (U1, U2, and posed quality-driven cross-layer optimized sys- U3) over LTE cellular networks. tem with a scheduling algorithm, where the design goal is only to maximize the system throughput without consideration of the applica- tion QoS metrics such as delay constraint or By eliminating from the potential solution set video quality. the parameters that make the transmission delay As shown in Fig. 6, significant performance exceed the delay constraint, the constrained improvement is achieved by using the proposed problem can be relaxed to an unconstrained quality-driven cross-layer optimized system. optimization problem. Also, most decoder con- When channel quality decreases, the overall cealment strategies introduce dependencies received video quality in terms of PSNR among slices. For example, if the concealment decreases accordingly. However, the proposed algorithm uses the motion vector of the previous system achieves higher performance gain in MB to conceal the lost MB, it would cause the this case. To explain, while the existing system calculation of the expected distortion of the cur- tries to maximize the system throughput, which rent slice to depend on its previous slices. There- leads to more packet losses and thus perfor- fore, we can assume that the current slice mance degradation, the proposed system can depends on its previous z slices (z ≥ 0). Given holistically and dynamically adapt to the vary- the current decision vectors, the selection of the ing channel quality with optimal MCS schemes next decision vector is independent of the selec- and encoding parameters to ensure the best tion of the previous decision vectors, which received video quality under the delay con- makes the future step of the optimization pro- straint. In addition, the proposed system also cess independent of its past steps, forming the achieves higher performance gain when the foundation of dynamic programming. Thus, the delay constraint is more stringent. This is problem can be converted into and solved as a because the existing system is not aware of the well-known problem of finding the shortest path application type or its QoS, so it does not con- in a weighted directed acyclic graph (DAG) [9]. sider the application requirement during In this way the optimization problem can be effi- resource allocation. Thus, a more stringent ciently solved. delay constraint can lead to performance degradation because more packets cannot meet the playback deadline and more error conceal- EXPERIMENTAL ANALYSIS ments are needed. However, in the proposed In this article video coding is performed by system, delay constraint is integrated into the H.264/AVC JM 15.1 codec. The video sequence resource allocation algorithm as a negative “Foreman” is adopted for performance analysis, design factor. The more stringent the delay which is transmitted by all users in a cell simul- constraint, the larger the decision weight for taneously. For these users, the first 100 frames the user; thus, more resources will be assigned of the QCIF (176 × 144) video clip are coded at to the user. a frame rate of 30 frames/s, and each I frame is Overall, the proposed system is especially followed by 9 P frames. When a packet is lost suited to large-scale delay-sensitive real-time during transmission, the temporal replacement video delivery with less than good wireless chan- error concealment strategy is used. The pixels in nel conditions, or large-scale systems with many the previous frame, pointed to by the estimated classes of services that have different QoS motion vector, are used to replace the missing requirements. 108 IEEE Communications Magazine • February 2010
  8. 8. CONCLUSIONS ferences such as IEEE INFOCOM, IEEE GLOBECOM, and IEEE ICC. The proposed system In this article an extended QoS-aware OFDMA SONG CI [S‘98, M‘02, SM‘06] (sci@engr.unl.edu) is an asso- scheduling algorithm has been proposed for real- ciate professor in the Computer and Electronics Engineer- is especially suited time video delivery over LTE cellular networks, ing Department of the University of Nebraska-Lincoln. He is to large-scale director of the Intelligent Ubiquitous Computing Laborato- in which system throughput, application QoS, ry (iUbiComp Lab) and holds a courtesy appointment of delay-sensitive and scheduling fairness have been jointly consid- UNL Ph.D. in the Biomedical Engineering Program. He is ered to perform radio resource allocation for also affiliated with the Nebraska Biomechanics Core Facility real-time video multiple users. Then, based on the allocation at the University of Nebraska at Omaha and the Center for Advanced Surgical Technology (CAST) at the University of delivery with less result, a cross-layer optimized system has been Nebraska Medical Center, Omaha. His research interests presented to achieve the best user-perceived than good wireless include dynamic complex system modeling and optimiza- video quality under the given delay constraint by tion, green computing and power management, dynami- channel conditions, dynamically adapting to the instantaneous chan- cally reconfigurable embedded systems, content-aware quality-driven cross-layer optimized multimedia over wire- or large-scale nel quality. The cross-layer optimized result has less, cognitive network management and service-oriented also been used to improve the performance of architecture, and cyber-enable e-healthcare. He is an Asso- systems with many radio resource allocation. The experimental ciate Editor of IEEE Transactions on Vehicular Technology Classes of Services results have shown a significant performance and serves as a Guest Editor of IEEE Transactions on Multi- improvement of the proposed system. media (Special Issue on Quality-Driven Cross-Layer Design that have different for Multimedia Communications); Guest Editor of IEEE Net- work (Special Issue on Wireless Mesh Networks: Applica- QoS requirements. ACKNOWLEDGMENTS tions, Architectures, and Protocols); and Associate Editor of This work was partially supported by NSF under four other international journals. He served as TPC co-chair of IEEE ICCCN 2009 and TPC member for numerous confer- grant no. CCF-0830493 and NSFC under grant ences. He is Vice Chair of the Computer Society of the IEEE no. 60972083. Nebraska Section and is a member of the ACM and the ASHRAE. He is the SIG chair of the Quality-Driven Cross- Layer Design of Multimedia Communications Technical REFERENCES Committee (MMTC) of the IEEE Communications Society. [1] H. Wang et al., 4G Wireless Video Communications, Wiley, 2009. D ALEI W U [S‘05] received his B.S. and M.Eng. degrees in [2] C. So-In, R. Jain, and A. Tamini, “Scheduling in IEEE electrical engineering from Shandong University, Jinan, 802.16e Mobile WiMax Networks: Key Issues and a Sur- China, in 2001 and 2004, respectively. He is currently pur- vey,” IEEE JSAC, vol. 27, no. 2, Feb. 2009, pp. 156–71. suing a Ph.D. degree in the Department of Computer and [3] R. Kwan, C. Leung, and J. Zhang, “Multiuser Scheduling Electronics Engineering of the University of Nebraska-Lin- on the Downlink of an LTE Cellular System,” Research coln. His research interests include cross-layer design and Letters Commun., Jan. 2008. optimization over wireless networks, wireless multimedia [4] N. Xu et al., “A MC-GMR Scheduler for Shared Data communications, large-scale system modeling and analysis, Channel in 3GPP LTE System,” IEEE VTC-Fall, Sept. and approximate dynamic programming. 2006, pp. 1–5. [5] S. Ali, K. Lee, and V. Leung, “Dynamic Resource Allocation JIANJUN WU graduated from Southwest Jiaotong University in OFDMA Wireless Metropolitan Area Networks,” IEEE with an M.S. degree in April 2001. He joined Huawei as a Wireless Commun., vol. 14, no. 1, Feb. 2007, pp. 6–13. wireless engineer in 2001. From 2001 to 2003 he was [6] E. Dahlman et al., 3G Evolution: HSPA and LTE for responsible for the baseband development of NodeBs for Mobile Broadband, Academic Press, 2007. WCDMA systems, and during this time he also led a team [7] H. Luo, D. Wu, and S. Ci, “TFRC-based Rate Control for to design and develop smart antennas for WCDMA and Real-Time Video Streaming over Wireless Multi-Hop CDMA2000 systems. From 2003 to 2004 he worked on the Mesh Networks,” IEEE ICC, June 2009. Huawei B3G project as a system engineer, responsible for [8] R. Zhang, S. L. Regunathan, and K. Rose, “Video Coding system design. Since July 2005 he has led the Huawei with Optimal Inter/Intra-Mode Switching for Packet WiMAX Research project, and was responsible for the stan- Loss Resilience,” IEEE JSAC, vol. 18, no. 6, June 2000, dard research within the IEEE and WiMAX Forum. Since pp. 966–76. May 2007 he has been responsible for the system and [9] G. M. Schuster and A. K. Katsaggelos, Rate-Distortion architecture evolution research. He is chief scientist and Based Video Compression: Optimal Video Frame Com- manager of the i-Wireless (Internet oriented wireless net- pression and Object Boundary Encoding, Kluwer, 1997. work) Program of Huawei. His research interest is architec- [10] Y. Andreopoulos, N. Mastronade, and M. van der ture of the future wireless Internet. He has published 114 Schaar, “Cross-Layer Optimized Video Streaming over research patents. He is also the leader of the system engi- Wireless Multi-Hop Mesh Networks,” IEEE JSAC, vol. 24, neer group for Huawei radio access network research. no. 11, Nov. 2006, pp. 2104–15. HUI TANG received his B.S. degree from Lanzhou University in 1992, his M.S. degree from the Institute of Computing BIOGRAPHIES Technology of the Chinese Academy of Sciences in 1995, and his Ph.D. degree from the Institute of Acoustics of the H AIYAN L UO [S‘09] received his B.S. degree from Dalian Chinese Academy of Sciences in 1998. He joined the initial Jiaotong University (formerly Dalian Railway Institute), planning efforts that led to China Netcom in 1998, and and his M.S. degree from Dalian University of Technolo- now he is the chief scientist of Netcom Broadband Net- gy, China. He is currently pursuing his Ph.D. degree in work Inc. Since 2004 he has become the founding director the Computer and Electronics Engineering Department of of the High Performance Network Laboratory of the Insti- the University of Nebraska-Lincoln. His research interests tute of Acoustics of the Chinese Academy of Sciences, and focus on networking and distributed systems. Previously, his team has undertaken several key national projects. he worked for Bell Laboratories, Lucent Technologies as a Since 2008 he has served on the executive committee of member of technical staff. He has served as a referee for the national key project “The Next Generation Broadband IEEE Transactions on Wireless Communications, IEEE Wireless Mobile Network.” He holds more than 20 patents Transactions on Vehicular Technology, and several con- in the area of broadband networks. IEEE Communications Magazine • February 2010 109

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