FAST: A Channel Access Protocol for Wireless

Video (and Non-Video) Traffic

Sohraab Soltani Intelligent Automation, Inc. Rockville, Maryland 20855 Email: ssoltani@i-a-i.com

Hassan Aqeel Khan, and Hayder Radha Department of Electrical and Computer Engineering

Michigan State University

Abstract-This paper presents the design of a new paradigm for a content-aware wireless MAC layer that is optimized for wireless video (first and foremost) while targeting fairness and stability among competing video traffic, and among video and non-video traffic. Hence, we refer to the proposed MAC frame­work as the FAST (Fair And STable) protocol. FAST employs two parameters for each packet, a quality value and a time-to­live value. Based on these parameters, FAST is designed on a muIticlass priority queuing system that classifies the incoming traffic according to the content of each traffic flow and further identifies different priorities within each video content. We develop analytical frameworks to formulate channel allocation based on video/non-video fairness and video stability require­ments as a joint bandwidth maximization and scheduling opti­mization problem. We incorporate these frameworks to design and simulate a content-aware channel access mechanism, which utilizes video traffic content classifications and users demand in conjunction with stability and fairness requirements at the MAC layer to allocate wireless channels to individual wireless users. Our simulation results show that FAST provides significant improvements in packet-loss-ratio, delay, overall fairness, and stability parameters when compared with leading access control mechanisms over 4GILTE environment.

I. INTRODUCTION

It has been well documented by many studies that realtime video is emerging as the most dominant traffic delivered over the Internet, in general, and wireless networks in particular. Many studies also predict that this trend of video traffic predominance will continue to grow many years and probably decades into the future. Real-time Internet video will reach 62% of consumer Internet traffic by the end of 2015. Further­more, consumers are becoming more demanding in terms of video quality. It is predicted that high-definition Internet video will represent 77% of video-on-demand by 2015.

This explosive growth in multimedia Internet applications is primarily a result of the rapid advances in high speed wireless access and the remarkable emergence of mobile applications allowing users to launch these applications using 4G/LTE networks. The CISCO visual networking index predicts that mobile data traffic will grow 26 times between 2010 and 2015, to 6.3 exabyte - a billion gigabytes - per month; and more importantly, mobile video will be 66% of mobile traffic by 2015. These and other staggering statistics about Internet and wireless video emphasize the urgent need for new paradigms for the support and delivery of wireless video. The

978- 1 -4673-2447-2/1 2/$3l.00 © 20 1 2 IEEE

East Lansing, Michigan 48824 Email: hkhan, radha@egr.msu.edu

limited bandwidth of wireless and mobile networks and the tremendous growth in wireless and mobile IP traffic further exacerbate the need for new solutions that can handle video traffic to heterogeneous wireless devices.

This paper presents the design of a new paradigm for a content-aware wireless MAC layer that is optimized for wireless video (first and foremost) while targeting fairness and stability among competing video traffic, and among video and non-video traffic. Hence, we refer to the proposed MAC framework as the FAST (Fair And STable) protocol. The proposed FAST MAC targets intra-stability and inter-fairness objectives among competing traffic flows at the base station. It ensures intra-stability for each video user by considering a time-ta-live parameter for the video packets to acquire precedence-access to wireless channels within the end-to-end deadline of the time-sensitive video application. This ensures high-quality and smooth video playback at the end-user. To that end, FAST periodically computes the average queuing delay of each video flow (at the base station) and determines the minimum required bandwidth rate for that flow (based on the time-to-live parameter) to ensure intra-stability.

The inter-fairness objective of the FAST MAC is to deter­lnine a fair channel access distribution among competing real­time video and non-video users. We develop a content aware channel access mechanism (at the base station) to identify and prioritize traffic flows based on the video packet quality

parameter and the non-video contents. The proposed FAST protocol is content-aware in the sense that it even identifies priorities within each video flow with respect to the importance of the video packet quality. FAST then partitions the available channel and allocates subchannels to each video/non-video pri­ority flow. FAST formulates the channel access mechanism as a joint bandwidth maximization and scheduling optimization problem. The proposed MAC is designed based on a multiclass priority queuing system that classifies and manages the traffic demand by users in prioritized fashion according to the content of each traffic flow and further identifies different priorities within each video content. In summary, the contribution of this paper is as follows:

• We develop an analytical model for the average waiting time of each priority video and non-video flow at the FAST MAC layer using multiclass priority queuing sys­tem.

• We develop analytical tools to formulate a content-aware channel allocation process optimized for wireless video as an optimization problem that ensures inter-fairness priority and intra-stability of video flows at the FAST MAC layer .

• We design and simulate a FAST content-aware channel access mechanism. This FAST-based mechanism utilizes video-packet quality classifications and users demand in conjunction with stability and fairness requirements. These requirements are incorporated at the FAST MAC layer to allocate wireless channels to individual flows in a fair manner that also maximizes stability for all the flows involved.

We conducted extensive evaluations using the LTE-Sim [1]. We evaluated FAST efficacy in comparison with the perfor­mance of the Proportional Fair (PF) [2] and Modified largest weighted delay first (MLWDF) [3] algorithms. The realtime video simulations were conducted by incorporating the SV C Joint Scalable Video Model (JSVM) reference software [4] into the simulator. The simulation results demonstrate a sig­nificant performance gain in terms of packet loss, delay and video playback quality under the FAST MAC. Overall, FAST reduces the packet loss ratio by significant margin (up to an order of magnitude under heavy load) with respect to MLWDF and PF approaches. Further, under heavy load traffic, FAST delivers 80% of the packets in less than 20ms of delay whereas for MLWDF only about 36% of the packets have delays less than 20ms. In addition, end-users gain 5dB-lOdB better video playback using the FAST MAC.

The rest of the paper is organized as follows: The multiclass priority queuing framework, the analytical approach for evalu­ating the average delay of each traffic flow at the MAC layer, and the integrated optimization formulation for the proposed FAST content-aware channel access mechanism are presented in Section II. The FAST design and its channel allocation and transmission process are presented in Section III. Section IV evaluates the efficacy of FAST in comparison with current MAC protocols through rigorous simulations. Related research efforts are outlined in Section V. Section VI concludes the paper.

II. MODEL FORMULATION

In this work, we consider a set of traffic flows arnvmg at the base station denoted by f i,··· , fT where T is the total number of priority flows in the systems. The priority i indicate the importance of flow Ii- The priority assigned to each flow is subject to change with respect to the dynamics of the system. Each flow f i consists of a set of packets with ascending time-to-live (TTL) values. The TTL values for each packet is a fixed value that is set by the owner of the flow (e.g., applicationihigher layer protocol). Another parameter for each flow is the demand Di(t) at time t. This parameter measures the required bandwidth rate to ensure sustainable delivery and intra-stability of fi at time t to the destination.

Consider a wireless channel with overall bandwidth B in bits-per-second (bps). The base station is to allocate this

Fig. 1: Priority queuing model for multiclass traffic arrivals at the base station MAC.

bandwidth to different users. The objective is to find the best channel partitioning scenario {Bl'··· ,BK}(t), to divide the bandwidth B into sub channels Bl,· . . , BK at time t; and to determine T(t): the number of priority flows to deliver at time t. Note that we assume orthogonal frequency-division multiple access (OFDMA) as the underlying system for the MAC layer [5].

A. Content Aware Priority Queuing Model

To achieve an optimal content aware channel partitioning scheme, we consider a priority queuing system M / S /1 illus­trated in Fig. 1 with T types of flows and service time S with density f5(. ). We consider preemptive-resume priority rule where higher priority flows are allowed to interrupt the service time of lower priority traffic; the service time of the lower priority traffic resumes at the point where it was interrupted. We integrate this queuing system into the FAST content-aware MAC framework as follow:

• Each arrival represents a traffic flow f i with priority i, arriving at the MAC layer in a homogenous Poisson manner with arrival rate Ai. Each traffic flow consists of a batch of packets with a random size. By convention, type i traffic flow packets has higher priority than type j flow packets if i < j (i.e, traffic flow type "one" has the highest priority).

• A server represents a single or multiple wireless channels allocated to a particular priority flow to transfer packets within that flow to the destination.

• Arriving traffic flows require random number of channels (servers).

• There are K channels available: the base station can only assign a total of K wireless channels to flows in the system.

Below we analytically develop the building blocks of this queuing model to formulate an optimization problem and determine the optimal content-aware channel allocation at the base station.

B. Expected Content Delivery Service E[SkJ Each service entity in our framework represents a single

wireless channel. Consider the kth wireless channel with band­width Bk bps. The delivery time for this channel has a negative exponential distribution fsk(t) with mean J.L;1 = B;l. Therefore, the expected service time E[SkJ for this channel

is 1/ Bk to transfer each information bit to the destination. For K channels the total expected service time is then

1 1 E [S K J = K = ----,K;:;---Lk=l fJk Lk=l B k C. Expected Content Delivery Service Residual E[RkJ

(1)

Suppose that a traffic flow arrives at time t. The server is busy and the kth channel is already assigned to another flow at time t. The expected content delivery service residual measures the average residual time from t until the time of the next service (i.e., the time stamp that the kth channel is free). This is the delay between the current delivery and the next delivery. Suppose that the length of the current content delivery is tx, then the arrival instant will be a random point within this length, i.e., it will be uniformly distributed within the time interval (0, tx). Let Rk represent the residual time to wait until the kth channel is available, then

dt P(t :s; Rk :s; t + dtlX = tx) = tx = iRk (t)dt, t :s; tx,

where iRk (t) is a residual content delivery density function. Consequently, the expected residual time for traffic delivery over the kth channel is:

E[S�J -1 E[RkJ = 2E[S kJ = fJk . (2)

D. Expected Delivery Delay for Content Priority i: E[WiJ The mean waiting time of a type i flow is denoted by E[WiJ

and E[LiJ is the expected number of type i packets waiting in the queue. Further, let Pi = AiE[SiJ. For the highest priority flow, it holds that

T E[W1 J = E[L1 JE[SlJ + LPjE[RjJ.

j=l That is, the expected delay for a packet of type "one" is the total of expected service time for each type "one" packet in the queue and the summation of weighted residual service times for all priority flow types. According to Little's law, E[L 1 J = A1E[W1J. Therefore,

",T E[RJ E[W1 J = L..,J=l P J J (3)

1 - PI Recall that this queuing system follows a preemptive rule

where higher priority flows can interrupt lower priority ser­vices. Consequently, for a type i flow, there do not exist lower priority due to the preemption rule. So we henceforth assume that Ai+! = . . . = AT = O. The waiting time of a type i customer can be divided into portions (Wi1 ' Wi2,' . . ). Now Wi is equal to the total amount of work in the system upon arrival, since we assumed that there are no lower priority flows. Observe that the total amount of work in the system does not depend on the order in which the flows are transmitted. Hence, at each point in time, it is exactly the same as in the system where the flows are served according to the non­preemptive priority rule. So (Wi1 ' Wi2, . . . ), and thus Wi have

the same distribution as in the system with non-preemptive priorities and, of course, with Ai+1 = . . . = AT = O. The waiting time of a type i customer divided into portions (Wi1 ' Wi2," . ), can be obtained by generalizing equation (3) where E(Wi) = E(Wi1 + Wi2 + . . . ). Through some rigorous mathematical deductions, we have:

E[waiting time for priority iJ =

L;=l pjE[RjJ (1 - (PI + ... + Pi))(1 - (PI + ... + Pi-I ))

(4)

For the total mean waiting time, we have to add the delivery time plus all the interruptions of higher priority traffic (e.g., residual delivery delays) during the service time:

",i. P E[RJ E[WiJ = � J=l J J i-I (1 - Lj=l pj)(1 - Lj=l Pj)

+ E[SiJ

(5) (1- L;=lPj)

Using the E[SkJ and E[RkJ deducted in equations (1) and (2), we have:

\ -2 -1 [1 ",i-1 \ -1 ] ",i-1 \ -2 /l ifJi + fJi - L..,j=l /ljfJj + L..,j=l /ljfJi E[WiJ = . . 2'

AifJi1 [1- L;:i A jfJj 1 ] + [1- L;:i A jfJj 1 ] (6)

Note that depending on how many channels are allocated to flow ii, the mean service rate for flow i would be a multiple of the wireless channel service rate: fJi 1 = C ifJ k 1 . The delay for flow i computed in (6) will be used by proposed FAST MAC to calculate the demand of flow i at time t, Di(t). E. Inter-Fairness within Priority

Recall that the MAC layer divides the available bandwidth spectrum B to different subchannels (k = 1"" ,K) and allocates a subset of these subchannels to each priority flow. Let us consider the set B(t) = {B1 (t), B2(t),'" ,BT(t)}, a channel allocation at time t; That is B = L:=l Bi(t). The objective of fairness within priority is that this allocation ensures fairness for all traffic flows considering the priority of each flow in the system. We formulate the fairness condition as follows: let us denote B J as a bandwidth unit equal to the minimum channel bandwidth that can be allocated (i.e., a single subchannel bandwidth is at least B J)' Therefore, any subchannel allocation would be a multiple of BJ and B = K X BJ. We define Bi(t) = C i(t) x BJ as channel bandwidth rate allocated to flow i at time t. We define utility function

(7)

This utility function is called a-fair where different values of a yield different ideas of fairness. For instance a = 2 reflects the minimum potential delay fairness (i.e., UBi (t) = B�(lt) ) where the allocated bandwidth directly influences the delay associated with delivering the ith flow packets. For lim a -+ 1, the utility function in (7) leads to proportional fairness (i.e.,

UBi (t) = log Bi (t» where if some flow i rate increases, then there will be at least one other flow j whose rate will decrease and further, the proportion by which it decreases will be larger than the proportion by which the rate increases for flow i. Similarly, using lim a ---+ 00 gives max-min fairness where channel allocation attempts to first satisfy the needs of the flows that get the least amount of bandwidth from the network (i.e. , priority flows with small demands).

It is important to note that the utility function in (7) influences the service and residual rates of the priority queuing model described earlier. For instance, a rate Bi(t) = c i(t)Bl governs the service rate in equation (1); for Bi(t) = ci(t)BI, E[Si] = c,d)BJ' F Integrated Optimization Formulation

The objective is to find an optimal channel allocation at each transmission time t that satisfies the stability and fairness requirements of different traffic flows. More specifically, the optimization problem seeks to maximize the allocated band­width rate for each flow while providing a prioritized and pre­emptive channel access scheduling service. Note that the FAST content-aware MAC mechanism is a channel partitioning MAC protocol designed for systems such as 4G/LTE OFDMA.

Consider a wireless channel with a total bandwidth sRectrum B that can be divided into K subchannels: B = L�=l Bk. Let I i k (t) represent the indicator for allocation of sub-channel k to flow i at time t. By definition, each subchannel cannot be assigned to multiple flows at the same time. That is for channel k we have,

T L Ii k(t) :s; 1 for k = 1, . . . ,K. (8) i=l

Let Bi(t) denote the allocated rate for flow i at time t. This rate increases as more subchannels are allocated to flow i. Assuming that each subchannel has a bandwidth of B l, then

K Bi(t) :s; L Ii k(t) x Bl for i = 1" . . ,T. (9)

k=l The content-aware channel access mechanism in this work

is a preemptive and priority based system where the flows with higher priority can preemptively suspend flows with lower priority. Let Di(t) and Dj(t) represent the demand of flows i and j at time t. Notice that Di (t) for each flow is determined based on the intra-stability condition (see Section III-B for more details). By convention, flow i has a higher priority than j for i < j. Consequently, the priority should be given to flow i to fulfill Di(t) and then to flow j. Therefore, the rate allocated to flow i with respect to its demand should be higher than the rate assigned to flow j with respect to Dj(t). Consequently,

Bi(t) B j(t) -(

-)

> -(-) for i, j = 1, ... ,T and i -I- j. (10) Di t - Dj t

Further, to maximize the bandwidth allocation and fairness among all flows, the allocated rate for each flow should not exceed its demand:

Bi(t) :s; Di(t) for i = 1", . ,T. (11)

Therefore the integrated optImIzation problem for FAST content-aware channel allocation to determine the B(t) {B1 (t), B2(t),'" ,BT(t)} is formulated as follows:

T maXfob j(B(t)) = max '\' UBi (t) (12) B(t) B,(t),.=l:T �

T (B (t))l-a = '\' ' subject to:

� I-a i=l K

Bi(t) :s; L I i k(t) x Bl for i = 1"" ,T k=l

T L Ii k(t) :s; 1 for k = 1, . . . ,K i=l Bi(t) > B j(t)

for i J' = 1 '" T and i --L J' Di(t) - Dj(t) , " -:-

Bi(t) :s; Di(t) for i = 1"" ,T Bi (t) � 0 for i = 1, , " ,T I i k(t) � 0 for i = 1, ' " ,T and k = 1, ' , , ,K

T K LL1i k(t) x Bl = K X Bl i=l k=l for i = 1" " ,T and k = 1" " ,K,

Notice that the objective function is maximized on B (t) = {Bi(t)},i = 1"" ,T according to the inter-fairness policy governed by a. Since fob j(B(t)) is a concave function and the constraints are convex and affine, the Lagrangian function is given by:

max fob j(B(t)) Bi(t)20,I.,dt)20

- (3l [Bi(t) -t, I i k(t) x Bll - (31 [B j(t)Di(t) - xi(t)Dj(t)] - (31 [Bi(t) - Di(t)]

(13)

- v [t,t, I i k(t) X Bl - K x Bll i = l..T;k = 1. .K.

Note that in (13) we write some of the constraints explicitly. In (13), there are no product terms involving Bi(t) and Ii k(t) together. Hence, we can decompose the problem into two separate sub-problems. The first one is the bandwidth

maximization problem in the form of,

max fob j(B(t)) - (3l Bi(t) (14) B,(t)20 - (31 [B j(t)Di(t) - Bi(t)Dj(t)],

This sub-optimization problem aims to maximize the allocated rate to each flow based on their demand and other constraints. The second sub-optimization however deals with channel allo­cation scheduling, which we refer to as the channel scheduling

•••

• • •

Fig. 2: FAST channel allocation and transmission process.

problem and is formulated as

K

max f3t L Iik(t) x BI (15) I,dt)?o k=l

- v [t t, Iik (t) x B I - K x B I 1 The expression in (15) uses the Lagrange multipliers as weights to establish a many-to-one mapping process to deter­lnine which sub-wireless channels k = 1, . . . , K are assigned to each priority flow i at time t.

III. FAST CHANNEL ALLOCATION AND TRANSMISSION

In this section, we describe the FAST MAC process, de­picted in Fig.2, including prioritization, stability assessments, channel allocation from the arrival of traffic flows at the MAC to the content delivery transmission over wireless medium using LTE PHY layer.

A. Content Prioritization

Consider T traffic flows arriving at the MAC queue. The content prioritization component classifies the priority types of these flows in an ascending order where type "one" has the highest priority. This component performs packet inspections for each flow to identify the content type of that packet. More specifically, the network layer header of a packet is read. The assumption is that the content type of each flow is made available to the MAC layer through a "type of service" (TOS) field. For video flows, this component further performs within

Priority Index Content

1 Network Control

2 Voice

3 SVC Base Layer

4 AVC I-Slice

5 AVC P-Slice

6 SVC Enhancement Layer

7 Text

8 AVC B-Slice

9 SVC Enhancement Layer 2

TABLE I: The FAST MAC priority table.

video prioritization where it assigns video packets to different priority levels based on their packet quality parameter.

The video packet quality-value represents the level of im­portance of the video packet to its overall video stream that it belongs to. Hence, the video packet quality-value can be a function of the video frame type (Intra coded or predicted) and the scalable video layer (base layer, spatial enhancement, temporal enhancement, etc.). For instance, for the scalable video coding (SV C) packets, the content prioritization gives higher priority to those packets containing the base layer and lower priority to packets containing different enhancement layers. Silnilarly, for the H.264/ AV C, the packets containing the I-slice get higher priority than those containing the P-slice and B-slice within each group of picture (GOP).

It is important to note that the application layer should embed the information regarding the content of the packet in the "option" field of the TCP or UDP segment and the network layer should relay this information to the MAC layer. Consequently, the content prioritization component rearranges a new set of flows according to a specific priority table. An example of such priority table is given in Table I. Note that standard protocols such as IEEE 802. l le [6] use a similar priority table (with no within intra-video priority distinctions); however, the priority table is a design parameter and can be updated based on the traffic types and/or the environment where FAST MAC is deployed.

B. Stability Demand Assessment

FAST intra-stability approach should guarantee that each video packet to be received by the decoder by a certain deadline time; otherwise, the packet becomes useless for the realtime application. Thus, the video packet time-to-Iive parameter will influence this deadline time and therefore the intra-stability condition for each wireless video user. The Sta­

bility Demand Assessment component calculates the demand for each flow in terms of bandwidth rate according to the intra­stability condition. This process is carried by inspecting the time-to-Iive values of the packets within each flow.

Consider a situation (see Fig. 1) where traffic flow i arrives at the MAC layer and enters a queue for type i priority. We assume that each traffic flow arrives with random-sized batch of packets. Each packet is associated with a time-to-Iive (TTL) value by the corresponding higher layer protocol. Let TT Lw represent the TTL value of the Uth packet in traffic flow i. The stability condition states that no higher layer application should suffer from information starvation. That is, each packet within a traffic flow i should be delivered to its destination within the specified TTL value. Let us define TT Li (t) as time-to-Iive value of flow i at time t. Further, let us assume that for U packets carried by flow i, TT Lil ::; TT Li2 ::; . . . ::; TT Lw. That is the packet which is at the front of the queue has the lowest TTL I. Therefore, for to ::; t ::; t 1 , TT Li (t 1 ) = TTLil - t. After the first packet has been delivered (e.g., at time tl)' then the time-to-Iive value at time t 2: h for the flow i will be TT Li (t) = TT Li2 - t. Consequently, the intra­stability condition is satisfied if and only if the waiting time for priority i deducted in (6) in the system at time is less than the TT Li (t) of that flow. That is

(16)

The demand assessment for flow i should be at the level to ensure the intra-stability condition in (16). Let Lj(t) represents the number of packets of flow j in the system at time t. Further, let Bj(t) represent the estimated bandwidth rate for flow j, j i=- i based on the previous channel allocations by the MAC layer. Let Di(t) represents the bandwidth demand for flow i at time t. The objective is to find Di(t), the feasible set of bandwidth rates for flow i that meets intra-stability condition in (16). Consider equation (6), the expected delay of flow i in the system. This delay at time t is deducted in accordance with the demand by substituting J.Li1 with Di(t). Recall that the demand and the service rate both represent the expected bandwidth rate given to flow i. Consequently,

For Wi(Di(t)) ::; TTLi(t), the feasible set for Di(t) will be a set of bandwidth rate greater than D i (t) *. To calculate Di(t)*, we let E[WiIDi(t)] = TTLi(t) and solve equation (17) at TT Li (t):

[Li(t)Di(t)2 + Di(t)Al(t) + A2(t)] - [Li(t)Di(t)Al(t)TTLi(t) + Al(t)2TTLi(t)] = O. (20)

The solution for the second order polynomial equation of (20) leads to the minimum demand for flow i at time t: D i(t):

D ;(t) = ± _ 1( ) [(Al(t)2 * Li(t)2 * TTLi(t)2 Li t

+ 2 * Al(t)2 * Li(t) * TTLi(t) + Al(t)2 - 4 * A2(t) * Li(t))(1 /2) - Al(t) + Al(t) * Li(t) * TTLi(t)]. (21)

Notice that since Di (t) 2: 0, equation (20) has a unique solution.

C. LTE Content Aware Channel Allocation and Transmission

After the flow priorities, demands and the fairness objective are determined, the channel allocation optimizer solves the bandwidth maximization problem in (14) and the channel

scheduling problem in (15). The result of this process indicates which flows should be transmitted and how many channels are allocated for those flows. Therefore, among T flows in the system, the channel allocation optimizer will choose m priority flows (i.e. , m ::; T) each with a specific channel bandwidth for transmission to m users: T(t) = m.

As illustrated in Fig. 2, the data packets (of selected priority flows) for each user is first converted from serial format to parallel (SIP) format and is assigned to different Orthogonal Frequency Division Multiple Access (OFDMA) subcarriers (subcarrier mapping). Note that based on the feedback from channel allocation optimizer, one or more subcarriers are

[ . . ] _ Li(t)Di(t)2 + Di(t)Al (t) + A2(t) . selected for each user. After taking the inverse discrete Fourier E W .ID .(t) - Li(t)Di(t)Al(t) + Al(t)2 ::; TTL.(t) transform (IDFT), the signal format changes from parallel (17) to serial format (PIS). The cyclic prefix (CP) is added to

where Li(t) is the number of packets in flow i queue at time end of the OFDMA frame which eliminates the inter-symbol t and interference from the previous OFDMA frame. The RF section

i-I transmits the signal over the associated wireless channel to

Al(t) = 1 - L Lj(t)fr;l(t). (18) each user. Upon the reception of the LTE signal by each user, CP is removed and SIP and M-point DFT are performed to retrieve the original data. At this point, each user (based on

(19) its allocated radio channel frequency determined by channel

allocation optimizer) would perform subcarrier de-mapping followed by PIS to get its data.

j=1 i-I

A2(t) = L Lj(t)B;2(t). j=1

I For well-known video packet sequences that include bi-directional (B) frames, some care might be needed for reordering the packets. This is especially the case for a flow that contains mixed types of video packets. For example, certain video coding results in decoding order (e.g., IPBB .. ) that is different from display order (e.g., lBBP'.). And if all of these packets are put into the same flow class (e.g., a base-layer video), then reordering is needed. Hence, packets reordering might be required in certain cases to meet this queueing constraint. However, if all packets in a certain flow belong to the same type (e.g., all packets are P frame packets), then such reordering is not required.

IV. SIMULATIONS

Performance analysis for the proposed protocol was con­ducted using LTE-Sim [1], which is a C++ based open source framework for simulating LTE networks. It contains an implementation of the complete LTE protocol stack and supports the transmission of multimedia applications in single as well as multi-cellular environments. For the calculation

of optimal bandwidth based on the constraints formulated in Section II and more specifically equation (12), we used the NLOPT toolbox [7], an open source library for non-linear optimization. The algorithm used for optimization was the sequential quadratic programming algorithm for nonlinearly constrained gradient-based optimization [8].

The stability demand assessment in equation (20) was also embedded in LTE-Sim. Since FAST is designed to minimize the loss of packets at the MAC layer due to access control, and it emphasizes on stability and fairness, we had to ensure reliability at lower layers to avoid drawing wrong conclusions about the proposed scheduling algorithm. Thus, in order to avoid incurring additional losses due to interference from other cells, all simulations employed a single cell scenario. Total bandwidth allocated to the cell was 10 MHz which corresponds to a total of 50 radio bearers available for data transmission at any particular time slot. Cell radius was set to 1 km with mobile subscribers following a random path at pedestrian speeds. To simulate realtime video traffic, H.264 encoded video traces were generated from the H.264 SV C Joint Scalable Video Model (JSVM) reference software [4]. JSVM traces were converted to a format compatible with the LTE-Sim with the help of the Scalable Video-streaming Evaluation Framework (SVEF) [9].

For a judicious comparison, popular LTE downlink schedul­ing algorithms were employed for video transmission over the wireless channel. This includes the Proportional Fair (PF) [2] and Modified largest weighted delay first (M-LWDF) [3] algorithms. The PF scheduling algorithm tries to maximize throughput while simultaneously ensuring fairness [1]. The M-LWDF algorithm is designed for real-time applications, it takes into account the QoS requirements of the flows and allocates the highest quality channels to realtime flows whose expected delay threshold is estimated to expire first [1]. The performance metrics used to evaluate the quality of the received video traffic include the average Peak Signal to Noise Ratio (PSNR) of the luminance (Y) component, packet loss ratio, and the CDF of the delay; which in this case is the difference between the time a packet arrives at the transmitters queue and the time it is received. For generating multiple flows of video data we used copies of the CIF foreman [10] video sequence which was encoded using a GoP structure of G16B3 (i.e. IBBBPBBBPBBBPBBBI).

A. Packet Loss

Packet losses, of both video and Voip flows, for a number of different scenarios were evaluated for all three algorithms. The packet loss ratio (PLR) for Voip data for all scenarios was less than 0.5% indicating that all algorithms do a remarkable job at protecting Voip data. For video flows however, we observed significant differences between the packet loss ratios for the three algorithms. The packet loss ratios of video traffic for varying number of voice and video flows are presented in Fig. 3. Under light traffic conditions (e.g. 20 Voip and 4 Video Flows) the PLR of all three algorithms is less than 1 %. However, as the number of incoming flows increases

Algorithm Number of I Frames P Frames B Frames Flows PLR PLR PLR

PF 73.71% 72.61% 61.51% MLWDF 40 voip, 12 64.56% 54.5% 32.34% FAST video 4.42% 6.12% 8.34% PF 67.14% 65.02% 55.29% MLWDF 50voip,8 60.62% 52.58% 30.01% FAST video 0.02% 0.61% 5.1% PF 74.4% 73.41% 64.34% MLWDF 50 Voip, 12 72.77% 65.25% 40.78% FAST video 4.52% 6.36% 24.59%

TABLE II: Average framewise PLR during different load conditions. Video sequence consisted of a total of 38 I frames, 40 P frames and 512 B frames.

the PLR starts to increase. It can be observed that the PF algorithm gives the worst performance for video data; this makes sense because it does not take the QoS requirements of the traffic flows. The MLWDF algorithm performs slightly better; however its performance is also not acceptable for video data. Meanwhile FAST channel allocation demonstrates significant improvement in terms of PLR. Therefore, along with maintaining low PLR for Voip FAST also ensures that the PLR for video data is also maintained below a tolerable limit.

For video data, the overall degradation depends on the type of the lost frames (I,P, B or enhancement layer frame); therefore, for video data we also evaluated the PLR of each frame type. The performance of different algorithms evaluated for three traffic scenarios is presented in Table-II. The results in Table-II highlight the weaknesses of the existing algorithms; because these algorithms do not distinguish between the type of video frames, a significant number of key frames are lost under heavy traffic conditions. For example, for the MLWDF algorithm the PLR of I frames is 72.77% when we have 50 Voip and 12 video flows. In contrast, FAST prioritizes based on the frame type and therefore penalizes B frames to create room for I and P frames thereby minimizing the loss of key frames which are critical for video applications at the cost of sacrificing the less important B frames. Also note that the number of P and B frames lost reduce significantly in comparison to PF and MLWDF.

Fig. 4 shows the average PSNR of the luminance (Y) component of the decoded video sequences2. For the case of 20 Voip and 4 video flows the PLR is 0% for all three algorithms and therefore, the PSNR equals the loss less PSNR which is 36.93 dB for the tested sequence. The PF algorithm gives the worst performance overall; the performance of both

2Note that loss of key frames (l or P) generally leads to a video decoding faIlure; meanwhile, reasonable PSNR values cannot be computed without reconstructing the received video sequence. Therefore. while computing PSNR, any key frames that were lost during transmission were assumed to be received correctly. and therefore. the reported PSNR reflects the degradation caused only by the loss of B-frames. Hence, this way of computing the PSNR represents a clear advantage for the PF and MLWDF algorithms since both suffer significant packet losses that would (with high probability) include losses of packets that belong to key frames. Despite this unfair advantage for PF and MLWDF, still FAST outperforms both algorithms in terms of PSNR.

Video PLR for FAST Video PLR for MLWDF Video PLR for PF

15 60

14 14

No. Voip Flows No. Video Flows No. Voip Flows No. Video Flows No. Voip Flows No. Video Flows

(a) FAST (b) MLWDF (c) PF

Fig. 3: Video PLR for FAST, MLWDF and PF algorithms under different load conditions.

Packet Loss Ratio Max Delay Threshold FAST MLWDF PF

10 ms 50.99% 70.78% 55.98% 50 ms 4.2% 42.57% 47.46%

100 ms 0.87% 35.43% 48.15% 150 ms 0.02% 24.08% 50.91% 200 ms 0% 21.22% 45.41%

TABLE ill Video Packet loss ratio for different values of maximum delay threshold for the case of 30Voip and lO video flows.

4O '---�---�--�--�-----r��==il I ���WDFI

" _FAST'�

30

10

o 20-Volp 4-Vid�-VOIP 10-Video 40-VOIp 10-Video 40-VOlP 12-Video 60-VOIp 12-Video

Fig. 4: Average PSNR of luminance component under different load conditions.

PF and MLWDF is significantly degraded under heavy load conditions with PSNR. Meanwhile, the proposed FAST algo­rithm maintains noticeably higher PSNR values.

B. Delay Performance

For multimedia content the average delay is a critical factor. Therefore, it is essential for any scheduling algorithm to pro­vide minimal delay along with low packet losses. Most modern multimedia scheduling algorithms take into consideration the delay requirements of the incoming data and assign higher priority to data that has more stringent delay requirements. The

MLWDF algorithm for example [3] computes the probability that the Head-of-the-line (HOL) delay of a packet exceeds the maximum delay threshold (Dmax) and assigns priority to flows whose packets have a higher probability of exceeding the delay threshold. Where the maximum delay threshold (Dmax) depends on the application.

FAST employs the time-to-live (TTL) parameter, which in our simulations, and for each particular flow was calculated as the difference between the maximum delay threshold (Dmax) (set by the packet owner) and the HOL packet delay of the flow. As the delay constraints become more stringent the performance of any scheduling algorithm should degrade. For this purpose, we evaluated the PLR of the algorithms in question for different values of the delay threshold under heavy load conditions, and the results are shown in Table III.

It can be observed that the packet losses for QoS based al­gorithms increase as the delay threshold is reduced. However, the performance degradation for FAST is still significantly lower than MLWDF and PF. Note that PF demonstrates similar performance for all values of delay threshold; this makes sense since PF is not a QoS based algorithm and does not take the QoS requirements of the application into consideration and therefore, its performance depends only on the traffic load. However, on comparing FAST and MLWDF it can be observed that FAST is clearly superior over MLWDF from this perspective also. It is also worth highlighting that MLWDF takes into consideration channel conditions as well. It assigns the best channels to flows whose HOL packets are about to be dropped. Under our simulations, FAST does not take channel conditions into consideration. It is anticipated that further performance gains can be achieved by using the channel quality information to assign the best channels to flows with the highest priority. Given in Figure 5 are the CDFs of the packet delay for the three algorithms under different traffic loads. When the load is light (i.e. 20 Voip, 6 videos) the delay performance is similar for all three algorithms; however, FAST performs slightly better than MLWDF and PF. For a heavier traffic load (30 Voip, lO videos) it can be seen from the CDF that FAST clearly outperforms other algorithms. Even under heavy load conditions 82% of the received packets have less than 40 ms of delay whereas for MLWDF only about 52% of

0.02

CDF (20-Voip, 6-Video Flows)

0.04 0.06 0.08 Delay (sec)

(a)

0.1 0.12

CDF (30-Voip, 10-Video Flows)

0.02 0.04 0.06 0.08 Delay (sec)

(b)

0.1 0.12

Fig. 5: CDF of video packet delay under for different load conditions.

the packets have delays less than 40 ms.

C. Internet Traffic

Although video traffic is becoming increasingly important, no channel scheduling algorithm can be accepted universally if it cannot transmit internet traffic efficiently. To test the performance of FAST for internet traffic, best effort data was transmitted for different traffic scenarios. One performance metric that was used for comparison is the well-known Jains fairness index. However, since FAST essentially tries to en­sure fairness between the different categories of applications therefore, instead of computing the fairness between individual users we computed the fairness between the three types of flows that FAST supports (voice, video and best effort). Given in Table-IV are the values of Jains Fairness Index for two traffic scenarios. Based on the fairness index alone it seems that PF is the best algorithm; however, since data in video flows is significantly larger than data in the other flows PF achieves this fairness by penalizing video traffic; which results in video PLR for PF around 54% for both scenarios in Table­IV. Therefore, we employ another measure which we call the Normalized Fairness Index (NFl). The Nfl is basically the product of the overall goodput, for the entire duration of the simulation, with the Fairness Index. From the perspective of NFl it is clear that FAST outperforms both MLWDF and PF for the given scenarios.

D. Content Prioritization

One of the key features of FAST is its ability to prioritize data based on the characteristics of the incoming traffic. For the simulations presented here, FAST operates by assigning the highest priority to voip data, followed by the I-frame flow, then the P-frames and then B-frames get the lowest priority. Fig. 6 gives a time series plot of the channel allocations done by FAST for different traffic types. The values have been normalized in order to plot them in the same figure. This figure illustrates a key operating principle of FAST; in the absence of

Traffic Type

20Voip

10

Video

10 BE

30Voip

10

Video

88E

11)

0.631

0.6904

FAST Goodput (Mbps)

12)

7.761

6.296

Nfl FI (Mbps) 11) (1°2)

4.897 0.659

4.347 0.5909

MlWDF PF Goodput Nfl FI Goodput Nfl (Mbps) (Mbps) 11) (Mbps) (Mbps)

12) (1*2) 12) (1·2)

4.751 3.131 0.6781 3.209 2.176

5.194 3.069 0.7258 2.892 2.099

TABLE IV Fairness Index, Goodput and Normalized Fairness Index for FAST, MLWDF and PF.

1.5 '--��-��-��-��-�----r=�VO"")Pc=il -I-Flow

-P-Flow -B-Flow

4.115 4.12 Time (sec)

Fig. 6: FAST channel allocations to different traffic types

any voice traffic, overall, the 1- and P- frames have the highest priority and are allocated more channels than the B frames.

Notice that with the onset of a burst of Voip packets the channels allocated to video frames drop. Also notice that P and B frame flows are penalized more significantly than the 1-frame flow thus verifying that I-frame flows have much higher priority. Meanwhile FAST also reacts based on the dynamics of the system in terms of content demand and TTL values; that is why, we observe that at some instances more channels are allocated to the P- and B- flows (although they have

lower priority) to compensate for their demands and satisfy the stability condition in (16). Overall, I-frame flows lose the least amount of channels with the onset of voip traffic and B­frame flows lose the most amount of channels. This behavior of FAST makes it the ideal candidate for scalable video content which has a larger number of priority levels; FAST is flexible to make adjustments under heavy load conditions.

V. RELATED WORK

It can be argued that improvements in all layers of the protocol stack, from physical to application, can have a pro­found impact on wireless video quality and efficiency. Some of these areas of improvements are receiving increasingly adequate attention [11]-[13]. For example, physical layer improvements provided by new and emerging technologies, such as WiFi and 4G/LTE, are significant steps in the direc­tion of addressing the need of wireless video. Furthermore, numerous efforts are being pursed [14]-[16] to improve the video coding efficiency within the relevant lTV and MPEG expert groups under the auspices of the High Efficiency Video Coding (HEV C) standardization effort [15]. HEV C is targeting 50% reduction in video bitrate relative to the state-of-the­art H.264 standard. There is a significant amount of work in improving the performance of MAC layer for IEEE802.11 WiFi and 4G/LTE technologies for realtime video delivery. For instance in [17], a timestamp-based content-aware adaptive retry (CAR) mechanism for MPEG video streaming over 802.11 WLANs is proposed where the MAC dynamically determines whether to send or discard a packet based on its retransmission deadline. Numerous solutions and architectures for cross-layer optimized multimedia transmission have been proposed (see for example [18]-[20]). To provide QoS for multimedia applications, the IEEE 802.11e [6] was defined as a supplement to the existing legacy 802.11 MAC sublayer. Popular LTE downlink scheduling algorithms for video trans­mission over the wireless channel include the Proportional Fair (PF) [2] and Modified largest weighted delay first (M­LWDF) [3] algorithms.

V I. C ONCLUSION

In this paper, we developed the FAST channel allocation mechanism. FAST is a content-aware approach optimized for video (as the primary traffic) and non-video delivery. FAST performs within video traffic prioritization to further enhance realtime traffic delivery and end-user playback quality. It jointly ensures fairness and stability for traffic delivery at the MAC layer. The simulation results show a significant performance gain and FAST efficacy in delivering video (and non-video) traffic to the end-users. The proposed FAST introduces a new paradigm in protocol design and implemen­tation for wireless communication by adapting to video traffic requirements (first and foremost) and by performing within video prioritization to provide sustainable content delivery. The authors aim to further extend the proposed FAST and analyze the potential approaches to embed FAST into current MAC standards.

REFERENCES

[I] G. Piro, L. Grieco, G. Boggia, F. Capozzi, and P. Camarda, "Simulating lte ceUular systems: an open source framework," Vehicular Technology, IEEE Transactions on, no. 99, pp. 1-1 , 20 1 0.

[2] E. Dahlman, 3G evolution: HSPA and LTE for mobile broadband. Academic Press, 2008.

[3] M. Andrews, K. Kumaran, K. Ramanan, A. Stolyar, P. Whiting, and R. Vijayakumar, "Providing quality of service over a shared wireless link," IEEE Comm. Magazine, vol. 39, no. 2, pp. 1 50-1 54, 200 1.

[4] I. Amonou, N. Cammas, S. Kervadec, and S. Pateux, "Optimized rate­distortion extraction with quality layers in the scalable extension of h. 264/avc," IEEE Trans. on Circ. and Syst. Video Tech. , vol. 17 , no. 9, pp. 1 1 86-1 193 , 2007.

[5] H. Holma and A. Toskala, LTE for UMTS: OFDMA and SC-FDMA based radio access. John Wiley & Sons Inc, 2009.

[6] S. Mangold, S. Choi, P. May, O. Klein, G. Hiertz, and L. Stibor, "Ieee 802.1 1 e wireless Ian for quality of service," in Proc. European Wireless, vol. 2, pp. 32-39, 2002.

[7] S. Johnson, "The nlopt nonlinear-optimization package, 2008," URL:

http://ab- initio. mit. edu!wiki!index.php!NLopt. [8] D. Kraft, "Algorithm 733: Tomp-fortran modules for optimal control

calculations," ACM Transactions on Mathematical Software (TOMS), vol. 20, no. 3, pp. 262-28 1 , 1 994.

[9] A. Detti, G. Bianchi, C. Pisa, F. Proto, P. Loreti, W. Kellerer, S. Thakol­sri, and J. Widmer, "Svef: an open-source experimental evaluation framework for h. 264 scalable video streaming," in IEEE Symp. Compo Comm. , pp. 36-4 1 , 2009.

[ 1 0] Online, "Hannover test sequences," URL: ftp:!!ftp.tnt. uni- hannover.de! pub! svc/ testsequences!.

[ I I ] G. Correa, D. Palomino, C. Diniz, L. Agostini, and S. Bampi, "Shbs: A heuristic for fast inter mode decision of h. 264/avc standard targeting vlsi design," in Multimedia and Expo (ICME). 2011 IEEE International Conference on, pp. 1-4, IEEE, 20 I I.

[ 1 2] X. Huang, R. Jin, and R. Cao. "A rate-distortion rate control algorithm based rho-domain for video coding," in Wireless Communications. Networking and Mobile Computing (WiCOM). 2011 7th International Conference on, pp. 1-4, IEEE, 20 1 1.

[ 1 3] F. Zhang and E. Steinbach, "Improved p-domain rate control with accurate header size estimation," in IEEE ICASSP, pp. 8 1 3-816, 20 1 1.

[ 14] Y. Chun, F. Min, and Z. Yan, "Fast mode decision algorithm for enhancement layer of spatial and cgs scalable video coding," in IEEE ICME, pp. 1-5, 20 1 1 .

[ 1 5] H. Choi, J. Nam, D. Sim, and I. Bajic, "Scalable video coding based on high efficiency video coding (hevc)," in Communications. Computers and Signal Processing (PacRim), 2011 IEEE Pacific Rim Conference on, pp. 346-35 1 , IEEE, 20 1 1.

[ 1 6] J. Leng, L. Sun, T. lkenaga, and S. Sakaida, "Content based hierarchical fast coding unit decision algorithm for hevc," in IEEE CMSP, vol. 1 , pp. 56-59, 20 1 1.

[ 17] M. Lu, P. Steenkiste, and T. Chen. "Video streaming over 802. 1 1 wlan with content-aware adaptive retry," in Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, pp. 723-726, IEEE, 2005.

[ 1 8] A. Majumda, D. Sachs. I. Kozintsev, K. Ramchandran, and M. Yeung, "Multicast and unicast real-time video streaming over wireless lans," IEEE Trans. Circ. Syst. for Video Techn. , vol. 12, no. 6, pp. 524-534, 2002.

[ 1 9] Y. Pei and 1. Modestino, "Multi-layered video transmission over wireless channels using an adaptive modulation and coding scheme," in Interna­tional Conference on Image Processing, pp. 1 009-1012, IEEE, 200 1.

[20] M. van der Schaar, S. Krishnamachari, S. Choi, and X. Xu, "Adaptive cross-layer protection strategies for robust scalable video transmission over 802. 1 1 wlans," Selected Areas in Communications, IEEE Journal on, vol. 2 1 , no. 10, pp. 1752-1763, 2003.