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Content-aware resource allocation and packet scheduling for video transmission over wireless networks

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Abstract A cross-layer packet scheduling scheme that streams pre-encoded video over wireless downlink packet access networks to multiple users is presented. The scheme can be used with the emerging wireless standards such as HSDPA and IEEE 802.16. A
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  IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 25, NO. 4, MAY 2007 749 Content-Aware Resource Allocation and PacketScheduling for Video Transmission over Wireless Networks Peshala Pahalawatta, Student Member, IEEE, Randall Berry, Member, IEEE, Thrasyvoulos Pappas, Fellow, IEEE, Aggelos Katsaggelos, Fellow, IEEE  Abstract   —A cross-layer packet scheduling scheme thatstreams pre-encoded video over wireless downlink packet accessnetworks to multiple users is presented. The scheme can be usedwith the emerging wireless standards such as HSDPA and IEEE802.16. A gradient based scheduling scheme is used in which userdata rates are dynamically adjusted based on channel quality aswell as the gradients of a utility function. The user utilities aredesigned as a function of the distortion of the received video.This enables distortion-aware packet scheduling both within andacross multiple users. The utility takes into account decodererror concealment, an important component in deciding thereceived quality of the video. We consider both simple andcomplex error concealment techniques. Simulation results showthat the gradient based scheduling framework combined withthe content-aware utility functions provides a viable method fordownlink packet scheduling as it can significantly outperformcurrent content-independent techniques. Further tests determinethe sensitivity of the system to the initial video encoding schemes,as well as to non-real-time packet ordering techniques.  Index Terms  —Wireless packet scheduling, video streaming,H.264, HSDPA, cross-layer design I. I NTRODUCTION S INCE the introduction of the first GSM networks, inter-est in high speed wireless data transmission has grownrapidly. The demand for higher data rates stems mainly fromthe need to stream high quality multimedia content to mobileusers. Multimedia content, and specifically, streaming video,requires per-user data rates of a few hundred kilobits per sec-ond in order to be of useful quality. Recent cellular standardssuch as HSDPA (High Speed Downlink Packet Access) [1],and IEEE 802.16 ( WiMAX  ) [2], aim to provide data rates thatenable multimedia communication over wireless networks.Many proposed cross-layer scheduling and resource alloca-tion methods exploit the time-varying nature of the wirelesschannel to maximize the throughput of the network whilemaintaining fairness across multiple users [3]–[7]. Thesemethods rely on the multi-user diversity gain achieved byselectively allocating a majority of the available resourcesto users with good channel quality who can support higher data rates [8]–[10]. Many of these methods, such as the Manuscript received May 15, 2006; revised December 1, 2006. This workwas supported by the Motorola Center for Seamless Communication atNorthwestern University.The authors are with the Electrical Engineering and Computer ScienceDepartment at Northwestern University (e-mail: pesh@ece.northwestern.edu).Digital Object Identifier 10.1109/JSAC.2007.070511. proportional fair rule for CDMA 1xEVDO, can be viewedas gradient-based scheduling policies [11]. In these policies,during each time-slot, the transmitter maximizes the weightedsum of each user’s rate, where the (time-varying) weightsare given by the gradient of a specified utility function. Oneattractive feature of such policies is that they require onlymyopic decisions, and hence presume no knowledge of long-term channel or traffic distributions. We focus on networkswhere a combination of TDM and CDMA or OFDMA canbe used to transmit data to multiple users simultaneously.We consider “per-user” system constraints which can dependon the capabilities of the mobile client devices. For suchnetworks, [12] discusses the implementation of gradient-basedscheduling schemes.In [12], the optimization over the available resources isperformed at each time-slot while taking into account thefading state of each user, at that time. The utility function usedin [12] is defined as either a function of each user’s currentaverage throughput, or of each user’s queue length or delayof the head-of-line packet. A queue-length based utility canbe employed for video streaming applications where the delayconstraints are stringent. Such a utility does not, however, takeinto account the content of each video packet. In multimediaapplications, the content of a packet is critical in determiningthe packet’s importance. In this work, we propose a content-aware utility function, which is even better suited for videostreaming applications, and compare its performance to thatof content-independent schemes.A wealth of work exists on video streaming in general, andon video streaming over wireless networks, in particular. Onearea, which has received significant attention has been that of optimal real-time video encoding, where the source contentand channel model are jointly considered in determining theoptimal source encoding modes [13]–[18]. A thorough reviewof the existing approaches to joint source channel coding for video streaming can be found in [19]. We, however, focuson downlink video streaming where the media server is ata different location from the wireless base station, and thevideo encoding cannot be adapted to changes in the channel.Therefore, we assume the video is pre-encoded and packetizedat the server. Packet scheduling for the streaming of pre-encoded video is also a well-studied topic [20]–[22], wherethe focus has been on generating resource-distortion optimizedstrategies for transmission and retransmission of a pre-encoded 0733-8716/07/$25.00c  2007 IEEE  750 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 25, NO. 4, MAY 2007 sequence of video packets under lossy network conditions. Theabove methods, however, consider point-to-point streamingsystems where a video sequence is streamed to a single client.Packet scheduling for video streaming over wireless net-works to multiple clients has conventionally focused onsatisfying the delay constraint requirements inherent to thesystem. Examples of such work are [23], [24] and [25]. Inthese methods, the quality of service of the received video ismeasured only in terms of the packet delay, or packet loss rate.Methods that do consider the media content can be found in[26]–[28]. In [26], a heuristic approach is used to determinethe importance of frames across users based on the frametypes (I, P, or B), or their positions in a group of pictures.In [27], a concept of incrementally additive distortion amongvideo packets, introduced in [20], is used to determine theimportance of video packets for each user. Scheduling acrossusers, however, is performed using conventional, content-independent techniques. In [28], the priority across users isdetermined as a combination of a content-aware importancemeasure similar to that in [27], and the delay of the Head Of  Line (HOL) packet for each user. At each time slot, all theresources are dedicated to the user with the highest priority.In the model considered here, per user resource constraintsor lack of available data, make it advantageous to transmit tomultiple users at the same time.Our main contribution is to propose a distortion awarescheduling scheme for packet-based video transmission over wireless networks where a combination of TDM and CDMAis used. The resource allocation scheme departs from theschemes discussed above in that it is performed at eachtransmission time slot based only on the instantaneous channelfading states of each user. We consider error robust datapacketization at the encoder and realistic error concealmentschemes at the decoder. We focus on the gradient-basedscheduling scheme proposed in [12] and introduce a content-based utility function that enables optimizing over the actualquality of the received video. Our method orders the encodedvideo packets by their relative contribution to the final qualityof the video, and assigns a utility for each packet, whichcan then be used by the gradient-based scheduling schemeto allocate resources across users.In Sec. II, we give a general overview of the system and alsoprovide some background on video packetization. In Sec. III,we present our main contribution, which is the distortion-based utility function. In Sec. IV, we discuss the resourcesand constraints inherent to the system and define the generalgradient based scheduling problem. A solution is summarizedin Sec. V. In Sec. VI, we investigate the performance of the scheduling scheme using both simple and complex error concealment schemes. We also discuss the sensitivity of thescheme to offline packet ordering schemes, and to differentvideo compression schemes. Some final conclusions and av-enues for future work are presented in Sec. VII.II. S YSTEM O VERVIEW Figure 1 provides an overview of the system discussed inthis paper. We begin with a media server containing multiplevideo sequences. We assume that each sequence is packetized SchedulerVideo InChannel FeedbackVideo OutUser 1 DecoderUser 2 DecoderUser K DecoderMedia ServerEncoded Source Wireless Channel BackboneNetwork Fig. 1. Overview of multiuser downlink video streaming system into multiple data units . Each data unit/packet is independentlydecodable and represents a slice of the video. In recent videocoding standards, such as H.264, a slice could either be assmall as a group of a few macroblocks (MBs), or as large asan entire video frame. Each slice header acts as a resynchro-nization marker, which allows the slices to be independentlydecodable, and to be transported out of order, and still bedecoded correctly at the decoder. Note that, although in termsof decoder operation, each slice is independently decodable,in reality, most frames of a compressed sequence are inter frames, in which MBs may be dependent on macroblocks of previous frames through motion prediction.Once a video stream is requested by a client, the packetsare transmitted over a backbone network to the scheduler at a base station servicing multiple clients. We assume thatthe backbone network is lossless and of high bandwidth.For simplicity, we assume that all users being served arevideo users. The scheduling rule can easily accommodateother traffic by assigning them different utility functions. Thescheduler uses three features of each packet, in addition to Channel State Information (CSI) available through channelfeedback, to allocate resources across users. They are, for eachpacket m of each client i , the utility gained due to transmittingthe packet (described later), the size of the packet in bits, b i,m ,and the decoding deadline for the packet, τ  i,m . The decodingdeadline, τ  i,m , stems from the video streaming requirementthat all the packets needed to decode a frame of the videosequence must be received at the decoder buffer prior to theplayback time of that frame. We can assume that multiplepackets (e.g., all the packets in one frame) have the samedecoding deadlines.Any packet left in the transmission queue after its decodingdeadline has expired must be dropped since it has lost its valueto the decoder. Assuming real-time transmission, the number of transmission time slots available per each video frame canbe calculated from the playback time for a frame (33msec for 30fps video), and the length of each time-slot (e.g., 2msecfor HSDPA). Note that, unlike video conferencing systems,video streaming applications can afford some buffer time at thedecoder before starting to play back the video sequence. Thisis important because, in a compressed sequence, the qualityof the first frame, which is intra coded, can have a significantimpact on the quality of the following inter coded frames of the same sequence.The next step in Fig. 1 is that of receiving and decoding thevideo. At this point, errors in the decoded image are introduced  PAHALAWATTA et al. : CONTENT-AWARE RESOURCE ALLOCATION AND PACKET SCHEDULING 751 due to the loss of packets in the wireless channel, or due to thedropping of packets from the transmission queue. These errorsare typically concealed using an error concealment technique.In general, error concealment techniques use spatial and tem-poral correlations in the video data so that pixels representedby lost slices are estimated using data from the received slicesof the current frame, or a previous frame. Therefore, error concealment introduces an additional dependency between theslices of the sequence.III. C ONTENT -A WARE U TILITY F UNCTION The main contribution of our work is to propose a utilityfunction for video streaming that accounts for the dependen-cies between video packets and the effect that each videopacket has on the final quality of the received video. Theutility function we propose is especially relevant since it canbe used in conjunction with the gradient-based schedulingscheme of [12] to enable content-aware resource allocationacross multiple users. In gradient-basedscheduling algorithms,packets with a larger first-order change in utility are givenpriority. The key idea in the proposed method is to sort thepackets in the transmission buffer for each user based on thecontribution of each packet to the overall video quality, andthen to construct a utility function such that its gradient reflectsthe contribution of each packet. A description of the processused to generate packet utilities is given below.At a given transmission time slot, t , for each user, i , wepick a group of  M  i available packets such that each packet m in M  i has a decoding deadline, τ  i,m , greater than t . Anobvious approach would be to pick the group of packets withthe same decoding deadline that compose the current frame, or group of frames, to be transmitted. We know that each packet m consists of  b i,m bits. Note that we are omitting the timeindex, t , for simplicity, since it remains the same throughoutthis discussion. Now, let Π i = { π i, 1 ,π i, 2 ,...,π i,M  i } be there-ordered set of packets in the transmission queue such that π i, 1 will be the first packet of the group to be transmitted. Let D i [ { π i, 1 ,π i, 2 ,...,π i,k i } ] denote the distortion given that thefirst k i packets in the queue are transmitted to user  i and theremaining ( M  i − k i ) packets are dropped prior to transmission.Then, we define the user utility for user  i after  k i packettransmissions as, U  i [ k i ] = ( D i [Π i ] − D i [ { π i, 1 ,π i, 2 ,...,π i,k i } ]) , (1)where D i [Π i ] is the minimum distortion for the frame thatoccurs when all packets in the group are received. Note that anew utility function will need to be calculated after these M  i packets are sent. The proposed scheme does not depend onthe metric used to calculate the distortion. In our numericalwork, we define the distortion to be the sum absolute pixeldifference between the decoded and error-free frames. For easeof notation, let Π i ( k i ) = { π i, 1 ,...,π i,k i } . Then, assuming asimple error concealment scheme (as described in Sec. VI-A), we can guarantee that the user utility function is concaveand increasing by iteratively choosing each additional packet π i,k i +1 such that the utility gradient is maximized, i.e., π i,k i +1 = arg max m/ ∈ Π i ( k i ) u i,m [ k i ] , (2)where, u i,m [ k i ] = D i [Π i ( k i )] − D i [ { Π i ( k i ) ,m }| Π i ( k i )] b i,m . (3)In (3), D i [ { Π i ( k i ) ,m }| Π i ( k i )] indicates that the distortionafter adding packet m may be dependent on the currentlyordered set of packets Π i ( k i ) from the same group. This willbe true if a complex error concealment technique is used atthe decoder (See Sec. VI-A.2).We use the utility gradients, u i,π i,ki +1 [ k i ] in the gradientbased scheduling framework in Sec. IV-C to ensure that theresource allocation will explicitly consider the improvementsin video quality for each user.IV. P ROBLEM F ORMULATION  A. Channel Resources and Constraints We consider a scheme where a combination of TDM andCDMA is used, in which at a given transmission opportunity, t , the scheduler can decide on the number of spreading codes, n i , (assumed to be orthogonal) that can be used to transmitto a given user, i . Note that n i = 0 implies that user  i is not scheduled for transmission at that time slot 1 (as inthe previous section, the time-slot index remains the samethroughout this section and is omitted for simplicity). Themaximum number of spreading codes that can be handled byeach user is determined by the user’s mobile device. However,the total number of spreading codes, N  , that can be allocatedto all users, is limited by the specific standard ( N  = 15 for HSDPA). In addition to the number of spreading codes,the scheduler can also decide on the power level, p i , usedto transmit to a given user. The total power, P  , that can beused by the base station is also limited in order to restrict thepossibility of interference across neighboring cells. Assuming K  total users, these constraints can be written as: K   i =1 n i ≤ N, K   i =1  p i ≤ P, and, n i ≤ N  i , (4)where N  i is the maximum number of spreading codes for user  i .Our basic assumption in this work is that the constraints of the system will be such that the transmitter may not be ableto transmit all the available video packets in the transmissionqueue of each user in time to meet their decoding deadlines.  B. General Problem Definition We assume that the channel state for user  i , denoted by e i , at a given time slot is known based on channel qualityfeedback available in the system. The value of  e i representsthe normalized Signal to Interference plus Noise Ratio (SINR)per unit power and can vary quite rapidly, and in a largedynamic range, over time. Therefore, we assume that e i willbe a different but known value at each time slot. Defining SINR i = p i n i e i to be the SINR per code for user  i at a given 1 In the case of other standards such as CDMA 1xEVDO, only one user can be assigned per time slot. It must be noted that the packet prioritizationscheme discussed in Sec. III is applicable to that case, as well.  752 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 25, NO. 4, MAY 2007 time, we can assume that the achievable rate for user  i , r i ,satisfies: r i n i = Γ( ζ  i SINR i ) , (5)where Γ( x ) = B log(1 + x ) represents the Shannon capacityfor an AWGN channel, where B is the symbol rate per code.Here, ζ  i ∈ (0 , 1] represents a scaling factor and determines the gap from capacity for a realistic system. This is a reasonablemodel for systems that use coding techniques, such as turbocodes, that approach Shannon capacity. Setting ˘ e i = e i ζ  i , wecan specify the achievable rates for each user as a function of the control parameters n i and p i as follows: r i = n i B log  1 + p i ˘ e i n i  . (6)Now the resource allocation problem becomes one of spec-ifying the n i and p i allocated to each user such that a targetrate, r i , can be achieved. In the following, we assume thatthe channel quality feedback and the modulation and codingschemes are sufficiently good to avoid losses due to fading. InHSDPA, hybrid ARQ can also be used to recover from losses. C. Gradient-Based Scheduling Framework  The key idea in the gradient-based scheduling technique isto maximize the projection of the achievable rate vector, r =( r 1 ,r 2 ,...,r K  ) on to the gradient of a system utility function[12]. The system utility function is defined as: U i = K   i =1 U  i , (7)where U  i is a concave utility function. In a content-independent scheme, U  i can be a function of the averagethroughput for user  i , or the delay of the head-of-line packet.In the proposed content-aware scheme, we define U  i to bea function of the decoded video quality as in (1). Now, thegradient based resource allocation problem can be written as: max r ∈C ( ˘e ,χ ) K   i =1 w i u i,π i,ki +1 [ k i ] r i , (8)where, as in (3), k i denotes the number of packets alreadytransmitted to user  i , and π i,k i +1 denotes the next packet inthe ordered transmission queue. The constraint set, C  ( ˘e ,χ ) ,denotes all the achievable rates given ˘ e , the vector containingthe instantaneous channel states of each user, and χ the set of allowable n = ( n 1 ,n 2 ,...,n K  ) and p = (  p 1 ,p 2 ,...,p K  ) , thevectors containing the assigned number of spreading codes,and assigned power levels, of each user, respectively. Here, w i indicates an additional weighting used to attain fairness acrossusers over time. In our numerical work, we have considereda content-based technique for determining w i based on thedistortion in user  i ’s decoded video given the previouslytransmitted set of packets (i.e., users with poor decoded qualitybased on the previous transmissions will be assigned larger weights in order to ensure fairness over time). Equation (8)maximizes a weighted sum of the rates assigned to each user where the weights correspond to the gradients of the specifiedutility function. After each time-slot, the weights will be re-adjusted based on the packets scheduled in the previous slot.The constraint set will also change due to changes in thechannel states.Now, taking into account the system constraints specified in(4), as well as the formula for calculating each user’s achiev-able rate specified in (6), we can formulate the optimizationproblem as: V   ∗ := max ( n , p ) ∈ χ V   ( n , p ) , (9)subject to: K   i =1 n i ≤ N, K   i =1  p i ≤ P, where: V   ( n , p ) := K   i =1 w i u i,π i,ki +1 n i log  1 + p i ˘ e i n i  , (10)and, χ := { ( n , p ) ≥ 0 : n i ≤ N  i ∀ i } . (11)  D. Additional Constraints In addition to the main constraints specified above, apractical system is also limited by some “per-user” constraints.Among them are, a peak power constraint per user, a maxi-mum SINR per code constraint for each user, and a maximumand minimum rate constraint determined by the maximum andminimum coding rates allowed by the coding scheme.All of the above constraints can be grouped into a per user  power constraint  based on the SINR per code for each user [12]. This constraint can be viewed as: SINR i = p i ˘ e i n i ∈ [˘ s i ( n i ) ,s i ( n i )] , ∀ i, (12)where ˘ s i ( n i ) ≥ 0 . For the purposes of this work, we consider cases where the maximum and minimum SINR constraints arenot functions of  n i , i.e, SINR i ∈ [˘ s i ,s i ] , as with a maximumSINR per code constraint. In this case, the constraint set in(11) becomes, χ := { ( n , p ) ≥ 0 : n i ≤ N  i , ˘ s i ≤ p i ˘ e i n i ≤ s i ∀ i } . (13)  E. Extension to OFDMA Although the above formulation is primariliy designed for CDMA systems, it can also be adapted for use in OFDMAsystems under suitable conditions. For example, a commonapproach followed in OFDMA systems, is to form multiple subchannels consisting of sets of OFDM tones. In the casethat the OFDM tones are interleaved to form the subchannels(i.e., interleaved channelization is used), which is the defaultcase, referred to as PUSC (Partially Used SubCarrier), in IEEE802.16d/e [2], we can assume that the SINR is essentiallyuniform across all the subchannels for each user. Then, thenumber of subchannels plays an equivalent role to the number of codes ( N  ) in the CDMA based formulation above. Further details on gradient based scheduling approaches with OFDMAcan be found in [29].  PAHALAWATTA et al. : CONTENT-AWARE RESOURCE ALLOCATION AND PACKET SCHEDULING 753 V. S OLUTION A solution to the optimization problem of the type givenin (9) for the case when the maximum and minimum SINRconstraints are not functions of  n i is derived in detail in [12].In this section, we will simply summarize the basic form of the solution.The Lagrangian for the primal problem in (9) can be definedas: L ( p , n ,λ,µ ) =  i w i u i n i log  1 + p i ˘ e i n i  + λ  P  −  i  p i  + µ  N  −  i n i  . (14)Based on this we can define the dual function, L ( λ,µ ) = max ( n , p ) ∈ χ L ( p , n ,λ,µ ) , (15)which can be analytically computed by first keeping n ,λ,µ fixed and optimizing (14) over  p , and then optimizing over  n .The corresponding dual problem is given by, L ∗ = min ( λ,µ ) ≥ 0 L ( λ,µ ) . (16)Based on the concavity of  V   in (9), and the convexity of thedomain of optimization, it can be shown that a solution tothe dual problem exists, and that there is no duality gap, i.e., V   ∗ = L ∗ .In [12], an algorithm is given for solving the dual problembased on first optimizing over  µ for a fixed λ to find, L ( λ ) = max µ ≥ 0 L ( λ,µ ) , (17)and then minimizing L ( λ ) over  λ ≥ 0 . For the first step, L ( λ ) can be analytically computed. The function L ( λ ) can be shownto be a convex function of  λ , which can then be minimizedvia a one-dimensional search with geometric convergence.VI. S IMULATION S TUDY  A. Error Concealment 1) Simulation Results Using Simple Error Concealment: We categorize as simple , any error concealment technique, inwhich data from packets within the same group, Π i , are notused for concealment of other lost packets within that group.For example, if each group consists of packets from one videoframe, then replacing the pixel values of MBs contained ona lost packet with pixel values from the same location in theprevious frame is a commonly used simple error concealmenttechnique. With such techniques, it can be seen that the packetordering scheme proposed in Sec. III will always provide thebest possible ordering of packets within a packet group, suchthat given only k i out of the total M  i packets are actuallytransmitted, Π i ( k i ) would be the set of packets that wouldlead to the highest decoded video quality.We performed simulations to determine the performancegain that can be expected by using the content-dependentpacket ordering and resource allocation scheme. Lost pack-ets were concealed using the simple concealment technique TABLE IS YSTEM P ARAMETERS U SED IN S IMULATIONS N N  i P  ˘ s i s i 15 5 10W 0 1.76dB described above. Six video sequences with varied content:“foreman”, “carphone”, “mother and daughter”, “news”, “hallmonitor”, and “silent”, in QCIF (176x144) format were usedfor the simulations. The sequences were encoded in H.264(JVT reference software, JM 9.3 [30]) at variable bit rates toobtain a specified average PSNR of 35dB for each frame. Allframes except the first were encoded as P frames. To reduceerror propagation due to packet losses, random I MBs wereinserted into each frame during the encoding process. Theframes were packetized such that each packet/slice containedone row of MBs, which enabled a good balance betweenerror robustness and compression efficiency. Constrained intraprediction was used at the encoder for further error robustness.Although the sequences begin transmitting simultaneously, weprovide a buffer of 10 frame times in order for the firstframe (Intra coded) to be received by each user. Therefore,the start times of the subsequent frames can vary for eachuser. If a video packet could not be completely transmittedwithin a given transmission opportunity, we assume that itcan be fragmented, and the utility gradient of the fragmentedpacket is calculated using the number of remaining bits to betransmitted.The wireless network was modeled as an HSDPA system.The system parameters used in the simulations are shownin Table I. HSDPA provides 2 msec transmission time slots.Realistic channel traces for an HSDPA system were obtainedusing a proprietary channel simulator developed at MotorolaInc. The simulator accounts for correlated shadowing andmultipath fading effects with 6 multipath components. For the channel traces, users were located within a 0.8km radiusfrom the base station and user speeds were set at 30km/h.Figure 2 compares the average quality of the received video,using 4 different methods for calculating the utilities in (8).The first sets w i = 1 for all i and uses the utility functionsdescribed in Sec. III. The second, is a modification of the first,where w i is set to be the distortion of the currently transmittedsequence of user  i to ensure fairness across users. The thirdmethod is only partially content-aware in that it orders thevideo packets of each user according to their importance.The resource allocation across users, however, is performedassuming that the utility gradients in (8) are proportional to thecurrent queue length in bits of each user’s transmission queue.The computational complexity of the first three methods isvery similar as they all use the proposed packet orderingscheme. The final method is similar to the conventionalcontent-independent scheduling techniques in which no packetordering is performed at the scheduler; Scheduling is againbased on queue sizes.Figure 2(a) shows the average quality across 100 framesover 5 channel realizations for each sequence. This showsthat the content-aware schemes significantly out-perform theconventional queue length based scheduling scheme. The
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