332 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

A Reservation Based Backoff Method for VideoStreaming in 802.11 Home NetworksYong He, Jie Sun, Ruixi Yuan, Member, IEEE, and Weibo Gong, Fellow, IEEE

Abstract—QoS provisioning for video streaming over 802.11home networks is challenging due to the tight bandwidth anddelay constraints. Currently, both 802.11e HCCA and EDCAhave their disadvantages when delivering video over 802.11 net-works. EDCA exhibits degraded QoS performance for increasednumber of stations, while HCCA has high complexity, and alsosuffers from the impact of inter-cell interference. In this paper,we propose a new backoff mechanism which is fundamentallydifferent from traditional random backoff methods in 802.11.The mechanism achieves resource reservation by reusing oneor multiple time slots for transmission in consecutive backoffcycles. The basic idea is borrowed from R-ALOHA, but severalmodifications are made to allow it to work in the context ofCSMA network. An additive increase multiplicative decreasebased algorithm is proposed to control resource allocation forvideo streaming in the 802.11 network. Theoretical analysis andnumerical simulations are conducted to validate the effectivenessof the method and the algorithm.

Index Terms—video streaming, Medium Access Control(MAC), backoff algorithm, resource reservation, video streaming,802.11 home networks

I. INTRODUCTION

THE EMERGENCE of video streaming over 802.11 wire-less networks creates renewed interests in design and

analysis of new MAC protocols towards QoS provisioning forvideo applications. Compared with data communications suchas web browsing and e-mail exchanges, streaming applicationshave more stringent requirements on the timeliness of deliv-ering data. For example, most video applications require amaximum end-to-end delay as 200ms, and for audio-involvedinteractive applications such as VoIP and video conference, theallowable end-to-end delay is reduced to tens of milliseconds.To ensure desired video quality at the end system, the

transporting wireless network should be capable of providingsatisfactory QoS for delivered video streams. In particular,since most video applications are sensitive to network delaysand jitters, it is a desired feature that a medium access control(MAC) protocol can provide bounded and deterministic end-to-end delays. However, currently the EDCA (enhance dis-tributed channel access) and HCCA (HCF controlled channelaccess) [1], both originally designed for QoS enhancementfor data delivery, are not suitable for the video communi-cations. In EDCA, since the backoff contention window for

Manuscript received 1 March 2009; revised 1 November 2009. The researchwork was supported in part by NSFC (60574087).Yong He and Ruixi Yuan are with TNList and the Center for Intelligent

and Networked Systems, Tsinghua University, Beijing, China (e-mail: hey-ong04@mails.tsinghua.edu.cn, ryuan@tsinghua.edu.cn).Jie Sun and Weibo Gong are with the ECE department, Univer-

sity of Massachusetts, Amherst, MA 01003 (e-mail: jsun@ecs.umass.edu,gong@ecs.umass.edu).Digital Object Identifier 10.1109/JSAC.2010.100405.

audio/video transmission is usually small, when the numberof contending stations increases, network collision rate risesremarkably, which leads to reduced throughput and large end-to-end delays. Moreover, since EDCA is essentially basedon binary exponential backoff (BEB), it suffers the issueof unfairness as demonstrated in [2]. The HCCA methodachieves QoS by polling does not have these issues, but ithas higher signaling overhead and implementation complexity,and also suffers adversely from collisions in the overlappingarea of two neighboring BSSs (Basic Service Sets) due tolack of inter-AP coordination. In addition, the HCCA mayunder-utilize the channel resources for variable bit rate (VBR)traffic [10]. Hence, designing a new MAC protocol with lowercomplexity than HCCA but more effective QoS support thanEDCA becomes an important issue to support effective videostreaming over 802.11 networks.

This paper presents a novel backoff method to enhancevideo streaming in 802.11 home networks. Our motivation isto introduce an effective reservation mechanism into CSMAnetworks, thus enhancing its capability of real-time datatransport. However, although reservation has been widely usedin TDMA networks due to its good QoS performance [20][21], it is not clear how this technique can be effectivelyused in CSMA networks. The challenge is, since CSMAnetworks emphasize on high availability and are designed tobe plug-and-play, it is inherently unsuited for reservation. Inthis paper, we overcome this issue and provide a novel andeffective solution to extend reservation to CSMA networks.Our contributions are summarized as follows.

1) We propose the Reservation based Backoff (ReB) as anew method for slot reservation in CSMA networks.ReB is essentially a backoff method, but it is fun-damentally different from traditional random backoffmethods in that a station can reasonably reuse a timeslot in consecutive backoff cycles to achieve resourcereservation. The basic idea in ReB is borrowed fromreservation ALOHA (R-ALOHA), but ReB introducesadditional mechanisms (including fixed backoff cycle,random selection of transmission timing and multipletransmission opportunities within a backoff cycle) to al-low R-ALOHA work properly and efficiently in CSMAnetworks. ReB relies on carrier sensing to reserve re-source. When interference from neighboring cells causesunsynchronized backoff decrement among stations, ReBreverts back to a random backoff method. But wheneverthe backoff process is synchronized, even in a shortperiod, ReB can use reservation to reduce collisions,hence achieving better performance than random backoffmethods.

0733-8716/10/$25.00 c© 2010 IEEE

HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 333

2) We propose a mathematical model to analyze the per-formance of ReB, such as the collision probability andnetwork throughput. Using this model, we obtain thecollision probabilities in both single-cell and multi-cellnetworks, and show that it converges to zero, henceachieving resource reservation. This model can also bereadily used to analyze R-ALOHA.

3) We introduce an additive increase multiplicative de-crease (AIMD) based control algorithm to ReB so as tomaximize the network throughput as well as improve itsfairness performance. We show that resource allocationfor video streaming can be readily achieved by properconfiguration of the parameters of the control algorithm.

Simulation results obtained from the ns2 platform showthat ReB achieves higher throughput than traditional randombackoff methods and outperforms HCCA and EDCA in termsof QoS performance. ReB increases the system complexitycompared to EDCA due to the introduction of control al-gorithm and resource allocation. However, unlike the HCCAmethod, both the control algorithm and the resource allocationin ReB are performed locally without negotiation with otherpeers. It does not increase signaling overhead, only someextra computation is required at the mobile station. Hence,the increased system complexity is reasonable.The rest of this paper is organized as follows. Section II

presents related work and Section III describes the proposedmethod. In Section IV we give a theoretical analysis ofour method, followed by a discussion of dynamical resourceallocation in Section V. We present our simulation results inSection VI and summarize our work in Section VII.

II. RELATED WORK

Considerable effort has been devoted to improveEDCA/HCCA towards QoS provisioning. In [4] Xiao etal. improve EDCA by introducing budget calculation toprotect existing video streams, and they also investigatethe issue of bandwidth allocation for video streams in [5].Hu et al. [6] propose MAC contention control to achieveproportional bandwidth allocation in EDCA. Other worksimprove EDCA by adjusting parameters of EDCA adaptivelyto channel state or congestion level, e.g., adaptive contentionwindow [7] or randomized inter-frame space [8]. Recently,Heusse et al. [9] propose idle sense which achieves betterfairness performance through dynamical adjustment ofcontention window size. Although these methods achievebetter performance than DCF/EDCA, they cannot providesatisfactory QoS for video streams due to the inherent natureof fully random backoff which leads to network collisions.Compared to EDCA, HCCA is a reservation based solution

and many works have been done to improve its schedulingalgorithm. Grilo et al. [10] propose differentiated serviceintervals for serving different streams, and Lo et al. [11]enhance bandwidth allocation algorithm using source rates.The scheduling algorithms for VBR traffic are proposed in[12], and in [13], techniques of subflow and linear program-ming are introduced to optimize scheduling algorithms. Statebased polling [14] and multi-step polling [15] are proposed tosolve the inefficiency of polling when no packets is sending.

All these scheduling algorithms still suffer collisions whenoverlapping BSSs exist. Moreover, they are of high complexitywhich is not practical for real applications.In addition, there are also some solutions that introduce

reservation into 802.11 DCF. In one approach, as used in [16]and [17], DCF is enhanced by requiring a station to advertiseits next backoff value, and reservation is achieved if otherstations do not choose the same backoff value as the advertisedone. In another approach, as used in [18], reservation isachieved by sequential channel access after an agreement.Both approaches require negotiation among stations to doreservation. In contrast, ReB is a fully local solution and doesnot need bilateral negotiation.ReB is fundamentally different from traditional random

backoff based and polling based MAC protocols. It achievesresource reservation like HCCA and TDMA, but it is also assimple and flexible as DCF and EDCA. Compared with HCCAand TDMA, ReB has time-varying slots for reservation, so theresource waste due to empty reserved slots is minimized; whencompared with DCF and EDCA, ReB suffers less networkcollisions and provides more effective QoS support fromresource reservation. In some special cases, ReB can revertsback to DCF (in case of frequent inter-cell interference) orR-TDMA/R-ALOHA (if all slots are of equal length).

III. PROTOCOL DESCRIPTION

A. Protocol Overview

Resource reservation is a well known technique widelyused in TDMA schemes to achieve high throughput and QoSprovisioning for ATM networks [22]. In a typical reservationTDMA (R-TDMA), the radio resource is organized as su-perframes with each superframe divided into multiple timeslots of equal length. A station can reserve one or moretime slots in each superframe for channel access. Resourcereservation can also be achieved by reservation ALOHA (R-ALOHA) [21], where a time slot is automatically reserved bythe station that successfully used it in the previous superframe.Since channel access in the reserved slots suffers less networkcollisions, TDMA is usually more appropriate for QoS-awareapplications than contention-based CSMA protocols.In this paper, we propose a Reservation based Backoff

(ReB) method in wireless CSMA networks. ReB shares thesame spirit with R-ALOHA, but extends it to the contextof CSMA networks, which is highly dynamic compared toTDMA networks. Define a backoff cycle as a period of timewhen the backoff counter decrements from a maximal valueto zero, and define its size as the number of time slots in abackoff cycle. ReB has the following features that distinguishit from traditional random backoff methods such as BEB(binary exponential backoff).

1) The size of the backoff cycle in ReB is constant andfixed. In traditional random backoff methods, the sizeof the backoff cycle is dynamic and variable.

2) Within the fixed backoff cycle, ReB tries to find properslots for its data transmission. In BEB, the station alwaysuses the last slot in the cycle for data transmission.

3) ReB reuses a slot in next backoff cycle if it resulted

334 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

successful transmission1 in previous backoff cycle. BEBdoes not have such a feature.

4) ReB allows multiple transmission opportunities withinone backoff cycle, while BEB allows only a single slotfor data transmission in one backoff cycle.

The principle of ReB is best illustrated by an example inFig. 1, where three stations A, B and C are contending for thechannel and they all have backoff cycle of size 8. In backoffcycle 1, station A, B and C randomly select slots (3, 3, and 6respectively) in the cycle. Since A and B select the same slot(slot 3), they collide with each other. In backoff cycle 2, stationA and B randomly select slots again (1 and 4 respectivelly),and are collision-free in cycle 2. Station C reuses its slot incycle 2. In backoff cycle 3, all stations reuse their slots selectedin previous backoff cycles, and resource reservation is nowachieved. In case that a station gets a transmission failure inprevious backoff cycle, due to various factors such as carriersense errors, clock skew and hidden terminals, it will randomlyselect a new slot in the current backoff cycle.As shown in Fig. 1, the length of a time slot in CSMA

networks can vary over time. It may be as short as severalmicroseconds, sufficient for the physical layer to performcarrier sense once, or as long as hundreds of microseconds,sufficient to complete a frame exchange sequence. Such atime-varying feature provides high efficiency for ReB overtraditional TDMA schemes in that the waste time in unusedtime slots is minimized.The slots selected for data transmission in a backoff cycle

are called triggering points (TPs). Assume that the size ofbackoff cycle is M , then a TP k is an integer between 0and M − 1 that identifies a time slot to be used for datatransmission in the backoff cycle. Each station may have oneor more TPs in each backoff cycle. We denote Qi as the setof TPs of station i and sloti(n) as the backoff counter at thenth time slot. The size of Qi is between 0 and M − 1. Giventhe definition, the condition to trigger a data transmission is

• If ∃k ∈ Qi, sloti(n) = k, send data;• Otherwise, leave this time slot idle.

That is, when station i decreases its slot count down to a TP k,it initiates a new data transmission. When its counter sloti(n)reaches zero, it is reset to M − 1, indicating another backoffcycle. Multiple trigger points indicate multiple transmissionopportunities within one backoff cycle.For each station, as long as its set of TPs not overlap with

other stations’ TPs, the station can have collision-free channelaccess at this set. Otherwise, if this set of TPs contain triggerpoints corresponds to the same time slot as any other stations,collision will occur.ReB achieves resource reservation by reusing a TP in the

triggering set in consecutive backoff cycles. A successfultransmission at TP k in previous backoff cycle keeps this TP inthe set, whereas a failed transmission causes a removal of thisTP from the set. A TP can also be removed due to expiration

1It is worth noting that in this paper, by a successful transmission we mean that atransmission is acknowledged by the receivers. If a transmission is not acknowledged byan ACK, it is treated as a failed transmission. Thus generally multicast/broadcast (B/M)transmissions will be treated as failed transmissions. This does not affect the effectivenessof ReB too much because most traffics in home network are unicast particularly foruplink transmissions. Nevertheless, for reliable B/M transmissions with ACKs, they canbe treated as successful transmissions.

if it is not used for a number of consecutive backoff cycles.Both elements and size of Qi are time-varying. The accessprocedure can also be described by a service ring with Mslots, as shown in Fig. 1. When a TP reserves a time slot, itwould lock onto the same slot on the service ring in subsequentbackoff cycles until collision occurs.Since resource reservation requires synchronized decrement

of backoff slot counters, ReB works well in single-hop net-works. In multi-hop networks, presence of hidden terminalscan cause unsynchronized changes of backoff counters amongstations, which prevents stations from identifying reservedslots. A key feature of ReB is that, it reverts back to randomselection of TPs upon failed transmissions, which makes itsbehavior close to a random backoff method. We evaluate theimpact of interference in multi-cell networks via theoreticalanalysis (Section IV) and simulations (Section VI), and weshow that ReB still outperforms traditional random backoffmethods in such circumstances, although its gain reduces dueto impaired reservation.

B. Selection of Triggering Points

In ReB, a critical issue is how to choose a proper TP into Qi

to reduce network collisions. Denote Q̄i as the complementaryset of Qi in [0, M − 1]. A simple way is randomly selectingan element from Q̄i as the new TP. This approach is easyto implement but suffers high collision probability. ReB useshistorical channel access information to pick up a new TP.If a station observes that a slot was used in the last backoffcycle by other stations, it would not select that slot to senddata in current backoff cycle. It randomly picks a new TPfrom unused slots in Q̄i in the last backoff cycle with equalprobability. The overhead to maintain history information isacceptable. For M = 256, only 256/8 = 32 bytes of memoryis needed to record the information.

C. Additional Remarks

1. Typically, ReB can achieve long-term reservation ofchannel resource in single-hop networks but only short-termreservation in multi-hop networks. Such difference is dueto the interference from hidden terminals that breaks thesynchronization in multi-hop networks. The intensity of suchinterference affects the number of successful reservations inthe network and the duration of each reservation. For videostreams with bursty arrival pattern, short-term reservation canhave significant benefit in improving the QoS performance.2. ReB exhibits similarities in spirit with R-ALOHA. How-

ever, without the proposed modifications, R-ALOHA can notbe directly applied in CSMA networks. As we indicated,CSMA networks are often of high dynamics and resistantto reservation. Few prior works can be found in the liter-ature to address this issue. Our contribution in ReB is toapply the concept of dynamic reservation from R-ALOHA tocarefully control the backoff process, and employ the relativesynchronization induced by carrier sense in CSMA networksfor reservation of time slots. If we do not reserve slots in ReB,then it reverts back to a random backoff method, whereas ifwe treat each slot as time-invariant (like TDMA networks),then ReB is more like a R-ALOHA solution.

HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 335

Successful transmissioncollision Idle slot

43

Backoff cycle 1

AB

12 70

Where reservation happens !!!

65

C

3

A B

1 2 70 65

C

4 3

A B

1 2 70 65

C

4

Backoff cycle 2 Backoff cycle 3

6.25%6.25%

6.25%

6.25%

6.25%6.25%

6.25%6.25%

12.5%12.5%

12.5%

B

C

Ring size=8

Access procedure on the timeline Access procedure on the service ring

0

1

4

25

6

3

7 A

Figure 1. Resource reservation achieved by ReB on the discrete timeline and service ring.

3. The backoff window size M should be chosen to allowsufficient TPs in the network to send data. For home networkenvironments, 256 or 512 is large enough for M since anetwork usually involves up to dozens of stations. Moreover,M can be dynamically controlled by a central coordinator(e.g., AP) based on the number of stations associated to thenetwork.

IV. PERFORMANCE ANALYSIS

This section presents theoretical analysis for ReB. Differentfrom what the prior works [19], [20] have done for R-ALOHA,our analytical results are derived using inclusion-exclusionprinciple [24]. This analytical model can be readily appliedto R-ALOHA as well.

A. System Model

Consider a cell with L stations sharing the same mediumwith backoff cycle size M . Each station has its triggering setQi (1 ≤ i ≤ L) with size |Qi|. Define N as the total numberof TPs in the network, N = |Q1| + |Q2| + · · · + |QL|. Wemodel a back-off cycle using a service ring with M slots,labeled from 0 to M − 1 (as shown in Fig. 1). In this model,a pointer moves forward by a slot around the ring after eachtime slot passes on the practical system. Each TP correspondsa slot in the ring. Once the pointer reaches a slot correspondingto a station’s TP , that station has an opportunity to transmitdata.Three types of slots are defined in the service ring:• empty slot: a slot chosen by no TP;• collision-free slot: a slot chosen by exactly one TP;• collision slot: a slot chosen by two or more TPs.

A collision-free slot corresponds to a successful data transmis-sion while a collision slot corresponds to a network collision(no channel errors are considered in our analysis).

B. Collision Probability

We first summarize some notations used in our analysis.• N : total number of TPs in the network;• M : size of the service ring;• n: size of “Static TPs” (S-TP) in the network;• h: size of “Random TPs” (R-TP) in the network, h =

N − n;

• Ω: set of all slots in the ring, |Ω| = M ;• ΩR: set of reserved slots in Ω, |ΩR| = n;• ΩU : set of unreserved slots in Ω, |ΩU | = M − n;• X : set of collision-free slots;• k: size of X (|X |), which is a random variable;• XR: set of collision-free slots in ΩR;• XU : set of collision-free slots in ΩU , |XR| + |XU | =|X | = k, where 0 ≤ |XR| ≤ min{n, k}.

A triggering set contains two types of TPs, static TPs (S-TPs) and random TPs (R-TPs). In a backoff cycle, a S-TPlocks onto the same slot as in the previous backoff cycle,indicating successful reservation of channel resources, whilean R-TP fails to lock onto the same slot. An R-TP canbe a new TP that is selected into the triggering set due tothe need to expand its triggering set or due to collisionsin previous backoff cycle. In both cases, this station knowsthe reservation information and selects a new slot from theunreserved slots (ΩU ) for this R-TP. An R-TP can also bean old TP caused by inter-cell interference, in which case astation loses synchronization with other stations, thus this R-TP can unintentionally select a slot from both reserved slots(ΩR) and unreserved slots (ΩU ) in the ring.Given h R-TPs and n S-TPs in a backoff cycle, we first

calculate the probability to yield k collision-free slots in Ω,expressed as Ψh

N,M{|X | = k} or ΨhN,M(k).

We start with a simple case when no inter-cell interferencepresents. In this case, R-TPs randomly select slots fromunreserved slots and all reserved slots are collision-free;|ΩR| = |XR| = n. The problem is simplified to computethe probability to have |XU | = k−n collision-free slots whenh R-TPs are selecting slots from M − n unreserved slots inthe ring. We derive Ψh

N,M(k) using the following lemma.Lemma 1: Given N R-TPs, each one randomly selects a

slot from M slots, the probability to have exactly k (0 ≤ k ≤min(M, N)) collision-free slots is given by

Γ(N, M, k) =N∑

j=k

(−1)j+k

(N

j

)(M

j

)(j

k

)j!(M − j)N−j

MN

(1)

Proof: We label each R-TP as 1, 2, · · · , N . Let xi (1 ≤i ≤ N) denote the event that R-TP i selects a collision-freeslot and x̄i denote the event that R-TP i selects a collision

336 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

slot. Clearly

PN,M{|X | = k} =∑

1≤i1≤···≤ik≤N

P{xi1 · · ·xikx̄ik+1 · · · x̄iN }

(2)

where i1, · · · , iN is a permutation of (1, 2, · · · , N). Theinclusion-exclusion principle [24] gives

PN,M{ |X | = k} =N∑

j=k

(−1)j+k

(j

k

) ∑1≤i1≤···≤ij≤N

P{xi1 · · ·xij }(3)

where P{xi1 · · ·xij} is the probability that for the selectedj R-TPs (out of N R-TPs), i1, · · · , ij , each is collision-freeduring the slot selection procedure. It is easy to derive that

P{xi1 · · ·xij} =(

M

j

)j!

M j

(M − j

M

)N−j(4)

Since i1, · · · , ij are randomly chosen from (1, 2, · · · , N), wehave

∑1≤i1≤···≤ij≤N

P{xi1 · · ·xij} =(

N

j

)(M

j

)j!

M j

(M − j

M

)N−j.

(5)

Substituting (5) into (3) yields the result.In single cell case, Ψh

N,M(k) |single cell can be expressed asΨh

N,M (k) |single cell= Γ(h, M − n, k − n) (6)

Next, we consider the case when a portion of R-TPs arecaused by inter-cell interference. For explanation, we divide R-TPs into two subsets. One subset has TPs that select reservedslots from ΩR, denoted as ZR, and the other subset has TPsthat select unreserved slots from ΩU , denoted as ZU . Clearly,for X = XR

⋃XU , XR consists of reserved slots in ΩR that

are not selected by any R-TP in ZR, while XR consists ofunreserved slots in ΩU that are selected by exactly one R-TPin ZU . By first conditioning on |XR| and then on |ZR|, weexpand Ψh

N,M(k) |multi-cell asΨh

N,M(k) |multi-cell=min{n,k}∑

i=0

h∑j=n−i

P{|ZR| = j} · P{|XR| = i | |ZR| = j}

· P{|XU | = k − i | |ZU | = h− j},

(7)

where the first term P{|ZR| = j} in the summation is theprobability to have j (out of h) R-TPs that select reservedslots from ΩR,

P{|ZR| = j} =(

h

j

)(

n

M)j(

M − n

M)h−j (8)

and the second term P{|XR| = i | |ZR| = j} is theprobability to yield i collision-free slots in ΩR when j R-TPsin ZR select reserved slots from ΩR,

P{|XR| = i | |ZR| = j} =n−i∑l=0

(−1)l

(n

n− i

)(n− i

l

)(n− i− l

n)j (9)

0 0.2 0.4 0.6 0.8 10

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Reservation ratio θ

Col

lisio

n pr

obab

ility

single cell case (analytical)multi−cell case (analytical)random backoff schemesingle cell case (simulation)multi−cell case (simulation)

random backoff without reservation

multi−cell case

single cell case

Figure 2. The collision probability Pcoll(θ) versus the reservation ratio θ.N = 20, M = 50. The simulation runs in the context of 802.11b, with thesame parameter settings as in section VI

and the third term P{|XU | = k − i | |ZU | = h − j} is theprobability to yield k−i collision-free slots when h−j R-TPsin ZU (|ZU | = h − j) are selecting M − n unreserved slotsin ΩU ,

P{|XU | = k − i | |ZU | = h− j} =Γ(h− j, M − n, k − i)

(10)

Combining (7)-(10), ΨhN,M(k) |multi-cell can be obtained.

After getting ΨhN,M (k), given h R-TPs, the collision prob-

ability of a transmission can be computed as

Pcoll(h) = 1−N∑

k=1

k

N·Ψh

N,M (k). (11)

Definition 1: The reservation ratio θ is defined as the ratioof the number of static TPs to the total TPs used in a backoffcycle, i.e., θ = n

N = 1− hN .

The reservation ratio θ characterizes the degree of reser-vation achieved by ReB. We use Pcoll(θ) to represent thecollision probability in the following analysis.Fig. 2 shows Pcoll(θ) (0 ≤ θ ≤ 1) for both single-cell and

multi-cell scenarios. We set N = 20 and M = 50. Collisionprobability quickly converges to zero when θ approaches to1. The convergence rate in single cell case is faster than thatin multi-cell case. In contrast, a traditional random backoffmethod without reservation would retain a high collisionprobability in all cases. We also present simulation resultsin Fig. 2, where we have 20 stations in a cell and each hasone TP to send data. 20 × θ is the number of S-TPs andthe rest are R-TPs. In single cell case, an R-TP selects onlyfrom unreserved slots while in multi-cell case, an R-TP canselect any slot in the service ring. The agreement between thesimulation and analytical results demonstrates the validity ofour analytical model.Fig. 2 also reveals the reason why we apply reservation into

the backoff process. Backoff with reservation suffers muchlower collision probability than that without reservation, andmore reservation results in less collisions. As shown in thisfigure, ReB always has performance gain over random backoffmethods as long as the reservation ratio θ > 0.

C. Network Throughput

Define Ts as the average time of a successful frame trans-mission, and Tc as the average time of a collision. Denote

HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 337

0 5 10 15 20 250.3

0.4

0.5

0.6

0.7

Ratio M/N (N=10)

Nor

mal

ized

thro

ughp

ut θ=1(analytical)θ=0.9(analytical)θ=0.5(analytical)θ=0(analytical)θ=1(simulation)θ=0.9(simulation)θ=0.5(simulation)θ=0(simulation)

Figure 3. Throughput performance versus the ratio MNin the context of

802.11b. Parameter settings: Ts = 1.552ms, Tc = 1.327ms, l = 1500bytes,σ = 20us, N = 10. M increases from 10 until 256. Simulation parametersettings are the same as in section VI

l as the average payload size for a data frame and σ as theminimum duration of a physical time slot (for carrier senseonly). Then the saturation throughput, denoted as S, can beexpressed as the ratio of the effective payload transmitted ina time slot to the average time used for such a transmission.

SN,M =l · (1− Pcoll(θ))

MN · σ + Ts · (1− Pcoll(θ)) + Tc · Pcoll(θ)

(12)

Define normalized throughput as the ratio of saturationthroughput to the available bandwidth (i.e., the physical datarate). In Fig. 3, we depict the curves of normalized throughputfor a network with N = 10 stations when M increases from10 to 256. Different reservation ratios θ are considered. Asshown in the figure, for each θ, there exists an optimal ratioMN which maximizes the system throughput. When θ = 0,ReB degenerates to a random backoff method. In this case,the throughput is maximized when M

N = 6.8, which gives anumber of 5.8 idle slots in consecutive transmissions. This isconsistent with the results in [9]. For each analytical curve,we also give the corresponding simulation results on ns-2. Wesee that the analytical curves correlate well with the simulationcurves.

V. EFFICIENT RESOURCE ALLOCATION FOR VIDEOSTREAMING

Resource allocation in ReB is achieved by properly adjust-ing |Qi| at each station and reasonably balancing TPs amongvideo and other data applications.

A. Adaptive Triggering Set Size |Qi|The size of triggering set |Qi| is tuned adaptively to the

congestion level of the wireless channel. (12) indicates thatthe system throughput can be optimized by properly adjustingthe number of TPs (N ) in the network for a fixed backoffwindow size M . However, the exact value of N is difficultto obtain because the number of contending stations and theirTPs vary over time. Instead of estimating N , we evaluate thecongestion level by measuring the number of busy time slotsin the last backoff cycle.

Definition 2: The congestion index φ is defined as the ratioof the number of observed busy time slots m to the total Mtime slots in a backoff cycle, i.e., φ = m

M .1) Optimal Congestion Index φopt: We assume that a

station observes m busy time slots in the last backoff cycle,it has no knowledge of the real number of TPs, W , in thenetwork (note that a busy slot may be caused by a collision).By the Bayes’ formula, the probability that W takes on Ngiven m observed busy slots can be written as

P{W = N | m} =

P{m | W = N} · P{W = N}PMj=m P{m | W = j} · P{W = j} , m ≤ W ≤ M.

(13)

Without any prior knowledge about the distribution of W ,we assume W uniformly distributed2 in [m, M ], i.e. P{W =j} = 1/(M −m + 1), m ≤ j ≤M . Thus, the only unknownterm in (13) is P{m | W = j}, which is the probabilitythat m out of M time slots are selected by j TPs. By theinclusive-exclusive principle, we have

P{m | W = j} =

M

m

!(m

M)j

mXi=0

(−1)i

m

i

!(m − i

m)j (14)

The system throughput when W = N is given by SN,M

in (12). Hence, the expected throughput when we observe mbusy time slots can be derived as

S̄M (m) =M∑

N=m

SN,M · P{W = N | m} (15)

The expected throughput S̄M (m) is a function of m, θ, Mand l. Our investigation shows that S̄M (m) is not sensitive toM when M ≥ 100. Given θ and l, there exists an optimalcongestion index φopt = m0

M , where S̄M (m) is maximized atpoint m = m0. Theoretically, φopt can be obtained by solvingthe following differentiation equation.

d(S̄M (m)

)dm

|m=m0= 0, and φopt =m0

M(16)

However, it is difficult to derive the close-form expressionof φopt. Nevertheless, our numerical study shows that φopt

increases with θ but slightly decreases with l. It reaches globalminimum at φopt = 0.10, which corresponds to the smallestreservation ratio (θ = 0) and largest packet size (l = 1500).This optimal point, denoted as φ∗

opt, is useful when evaluatingthe network congestion level because it is the optimal pointin the worst case.2) Adaptive Control Algorithm: We propose an AIMD

(Additive Increase Multiplicative Decrease) based control al-gorithm to maintain the congestion index φ around the optimalpoint φopt. The challenge is, φopt varies with reservationratio θ and packet size l. In a practical system, it is almostimpossible to obtain the exact values of θ and l for eachbackoff cycle. To circumvent this problem, we employ a try-and-error approach. We use the minimal optimal point φ∗

opt asour threshold to probe the actual optimal point.

2One can try other types of distribution for W , but our investigationusing the symmetric triangle distribution (peaked at m+M

2) suggests that the

derived optimal congestion index φopt varies only slightly for such differentdistributions.

338 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

Our algorithm resembles the well-known TCP congestioncontrol algorithm. When we observed too many idle time slotsin the last backoff cycle (φ < φ∗

opt), we double |Qi|, whichin turn increases the congestion index φ. After φ reaches thethreshold φ∗

opt, the increase of |Qi| is retarded by switchingto the AIMD mode–if all data transmissions are successful inthe last backoff cycle, we additively increase |Qi|; whereas,once transmission failures occur in the last backoff cycle, wemultiplicatively decrease |Qi| until it reaches the minimumqmin. This yields the following algorithm. Note that α and βare control parameters for the AIMD control algorithm.

input : φ∗opt: threshould (minimal optimal point).

for each backoff round doObtain congestion index φ and the number offailures nf in the last backoff cycle;if φ < φ∗

opt then |Qi| ← |Qi| × 2;else if nf = 0 then |Qi| ← |Qi|+ α;

else |Qi| ← max( |Qi|β , qmin) ;

end

Algorithm 1: Adaptive triggering set size |Qi|

B. Resource Allocation for Video Streams

In Algorithm 1, qmin is the minimal number of TPs in astation’s triggering set in a backoff cycle. qmin can be tunedto meet the minimal requirement of video stream applications.Given an aggregated arrival rate of video streams as λvi pps(packet per second), and the average duration of a backoffcycle as τ seconds, the desired number of TPs qmin can becalculated by

qmin = min

(max(�(1 + ρ) · λviτ�, 1), Mφ∗

opt

)(17)

where ρ (ρ ≥ 1) is the surplus bandwidth to account forthe channel errors and possible network collisions during datatransmissions. ρ is set to 0.38 for a packet error rate of 10%with averaged packet size 1000 bytes [1]. λvi is obtainedby counting the number of video frames (denoted as nvi)arrived over a fixed time period Tvi, λvi = nvi/Tvi, whileτ is obtained by averaging the duration (denoted as Tm) ofevery m consecutive backoff cycles, τ = Tm/m.The value of qmin changes dynamically according to the

estimated values of λvi and τ . λvi expresses the demand ofchannel resources by the local video streams, and τ reflectsthe current channel utilization. Resource allocation for videostreams is achieved by properly leveraging qmin according tothe demand of local video streams and the global channelutilization. Both λvi and τ are estimated locally withoutnegotiation with any central coordinators, thus such resourceallocation runs in a fully distributed manner and has the ad-vantage of high scalability compared to traditional centralizedTDMA schemes [22] or HCCA [1] in 802.11e. Moreover, λvi

and τ are estimated online, so qmin changes quickly to thevariation of video streams, which makes it appropriate for bothCBR and VBR video streams. However, this is not an easy taskin traditional TDMA or HCCA protocols since the change ofchannel resource requires additional bilateral negotiation withcentral coordinators.

120o

120o

15m 15m50m

60o

Figure 4. Network topology when K = 3.

In (17), qmin is bounded byMφ∗opt to avoid over-congestion

in the network. Moreover, for other types of QoS-awareapplications such as voice over IP (VoIP) and network games,QoS can also be achieved in ReB by properly allocating thenumber of TPs for these applications.

VI. SIMULATION RESULTS

A. Simulation Methodology

We validate the effectiveness of ReB via simulation on ns2[25]. For comparison, we also implemented idle sense [9]and 802.11e EDCA/HCCA into the 802.11 module. For idlesense, the protocol-specific parameters are configured as in[9]. For HCCA, the reference scheduler in annex K of 802.11especification [1] is used, with a service interval of 20ms. ForEDCA, its parameter settings follow the recommended valuesin 802.11e specification (page 49 in [1]).We simulate a typical home network scenario where the

considered house is surrounded by several neighboring houses.The network in a house is interpreted as a cell. Each neigh-boring cell has an AP and 6 mobile stations, while theconsidered cell, called cell A, has an AP and L stations,with L varying in different simulation scenarios. Stations areuniformly distributed on a radius of 15m around AP, andneighboring cells are evenly distributed on a radius of 50maround cell A. The receiving and carrier sense range is set to30m and 40m respectively. By this configuration, stations inthe same cell can communicate with each other, but stationsin different cells may become hidden terminals if they arefar apart, or can interference with each other when they arenearly apart. Fig. 4 represents the network topology where thenumber of neighboring cells, denoted as K , is 3. Each cell hastwo stations that interfere with a neighboring cell. Note thatAPs of neighboring cells do not communicate with each other.This is because if APs can communicate with each other, APscan reduce inter-cell interference by channel management orinter-AP coordination [23].For stations in neighboring cells, each runs DCF for channel

access and each has a UDP stream to its AP. The packet

HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 339

5 10 15 20 25 3010.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

Number of stations in cell A

Norm

aliz

ed thro

ughput

ReB802.11 DCFIdle sense

(a) K = 0

5 10 15 20 25 3010.5

0.55

0.6

0.65

0.7

Number of stations in cell A

Norm

aliz

ed thro

ughput ReB

802.11 DCFIdle sense

(b) K = 3

Figure 5. Normalized Throughput as a function of the number of stations in cell A when K neighboring cells present.

Table ITHROUGHPUT GAIN OF REB WHEN A PART OF STATIONS IN CELL A GET INTERFERED BY NEIGHBORING CELLS

No. of STA get interferedtotal STA in cell A (=20) × 100% 0% 5% 10% 20% 30% 40% 50% 75% 100%

Throughput of DCF (SDCF ) 0.5409 0.5368 0.5312 0.5289 0.5201 0.5130 0.5065 0.4811 0.4692Throughput of idle sense (Sidle) 0.6117 0.5985 0.5958 0.5834 0.5727 0.5499 0.5313 0.5001 0.4743Throughput of ReB (SReB ) 0.7067 0.6980 0.6906 0.6740 0.6578 0.6307 0.5969 0.5596 0.5287

Gain of idle sense (Sidle−SDCFSDCF

) 13.09% 11.12% 12.16% 10.30% 10.11% 7.19% 4.90% 3.95% 1.09%

Gain of ReB (SReB−SDCFSDCF

) 30.65% 29.60% 30.01% 27.43% 26.48% 22.94% 17.85% 16.32% 12.68%

Reservation ratio θ for ReB 0.942 0.862 0.874 0.724 0.564 0.410 0.324 0.106 0.064

interval of these UDP streams follows Pareto distributionwith shape parameter 1.5 to simulate the bursty pattern.Packet size is fixed to 1500 bytes. For stations in cell A, theaccess protocol and traffic pattern used varies depending onsimulation scenarios. 802.11b DSSS (with data rate 11Mbps)is used for data transmission, and RTS/CTS is disabled as inpractical systems. The clear channel assess probability is setto 99%. To assess the impact of network collisions, no channelerrors are considered. Other parameters are set to defaultvalues as defined in 802.11 standards. Moreover, for ReB,we set backoff cycle size M to 256, and control parameter αand β to 1 and 1.5 respectively.

B. Throughput and fairness performance

We first investigate the throughput performance of ReB asa general MAC protocol in comparison with random backoffmethods, idle sense [9] and 802.11 DCF. In Fig. 5 we showhow the saturation throughput evolves with the number ofstations in cell A for case K = 0 and K = 3. The numberof stations in cell A increases from 1 to 30, each generatingsaturated traffic to a random destination in its cell. As shown inthis figure, ReB exhibits the highest throughput among threebackoff methods. Even compared with idle sense, ReB hasa throughput gain over 10% on average for both single cell(K = 0) and multi-cell (K = 3) cases. Such a throughputgain is due to the excellent reservation performance for ReB–its reservation ratio θ reaches 0.9 in single cell case and 0.5 inmulti-cell case. This yields lower collision rate for ReB than

random backoff methods (idle sense and DCF), according toanalysis in Fig. 2.

Table I shows throughput gain of idle sense and ReB over802.11 DCF in cell A with 20 stations. We set a topologysimilar to the case in Fig. 4, but with K = 6. The distancebetween cell A and neighboring cells is carefully reducedto gradually have more and more stations in cell A fallingin the interference area. The percentage of the interferedstations increases from 0% (none is interfered) up to 100%(all are interfered). We see that ReB has a throughput gainup to 30% over 802.11 DCF. Even when half of stations getinterfered by neighboring cells, it still achieves throughputgain around 18%, much higher than idle sense. When thenumber of interfered stations increases, the throughput gaindecreases. This is because the presence of interference reducesthe reservation ratio θ, as shown at the bottom row of TableI. Due to the congestion-aware control algorithm, ReB has athroughput gain around 10% over idle sense and DCF evenno reservation presents.

We have similar findings when we conduct simulations ofthroughput on 802.11 g/a platforms with a higher data rateup to 54Mbps. The superiority of ReB over idle sense andDCF lies in its capability to combat network collisions byreservation and does not depend on platforms.

It is worth noting that compared to idle sense and DCF, ReBis more sensitive to carrier sense errors. In 802.11 systemsthe carrier sense precision is expressed by the clear channelassessment (CCA) probability. Our study in a single cell with

340 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

5 10 15 20 25 3010.975

0.98

0.985

0.99

0.995

1

Number of stations in cell A

Fai

rnes

s in

dex

ReB

802.11 DCF

Idle sense

Figure 6. Fairness performance (using the Jain Fairness index in [3]).

6 nodes shows that, a decrease of 1% in the CCA probabilitywould cause a net decrease of around 1.6% in the throughputfor 802.11b DSSS, but merely 0.2% for 802.11 g/a OFDM.This is because the CCA time in 802.11 g/a is only 4us whichallows a station to conduct CCA twice within a time slot. Infact, many practical 802.11 chipsets have much shorter CCAtime than that specified in the standard, giving a sufficientlyhigh CCA probability to running ReB on these chipsets.We evaluate the fairness using the normalized Jain fairness

index in [3]. Fig. 6 shows the calculated fairness performancein a single cell when the number of stations increases from1 to 30. ReB has a good fairness performance comparable toidle sense, due to its use of AIMD algorithm to control thechannel access opportunities as idle sense does. 802.11 DCFhas the worst fairness performance among three methods.

C. Convergence Speed

When stations frequently join/leave the network, a stationchanges its number of TPs adaptively. Fig. 7 depicts how astation’s number of TPs evolves with the network size. At thefirst 50 seconds, two stations are present in cell A (stage 1). Atsecond 50, 8 stations join cell A making a total of 10 stations(stage 2). At the time epoch of 100 second, 9 stations leavecell A with only one left(stage 3). 50 seconds later another19 stations join cell A (stage 4) and 17 of them leave at timeepoch of 200 second (stage 5). The y-axis gives the numberof TPs of station 1, which is the only one that stays in cell Aover all stages. At each stage, all stations have sufficient datato send. We see that station 1’s number of TPs adapts quicklyto the traffic conditions–only few backoff off cycles are neededto reach a new stable state. Such adaptive control of TPs inthe network maximizes the channel utilization in cases of lessactive stations (stage 1, 3, 5) and avoids congestion in cases ofmore active stations (stage 2, 4) in the network. In all stages,the network congestion level oscillates between 0.1 and 0.35,and the normalized throughput is maintained around 0.7.

D. QoS Performance for Video Streams

This section presents the QoS performance. The number ofstations in cell A is fixed to 6. Each station has two types of

uplink traffics connecting to the AP, video traffic and httptraffic. The video traffic comprises 1 or 2 identical videostreams. A video stream is obtained by encoding CIF-sizedvideo samples using the MPEG-4 Simple Profile encodingscheme (by the MoMuSys reference encoding software [26]),with a rate of 15 frames per second, which gives a roughbit-rate of 384kbps. Each frame is further encoded using aGoV (Group of Video Object Planes) comprising one I framefollowed by 29 P frames. The encoded bitstream is packetizedin RTP packets with a maximum packet size of 1500 bytes.The well known video sequences foreman and mother arealternatively used to generate the video streams. The httptraffic is generated using the PackMime traffic model in [27]with an average rate of about 100kbps. In 802.11e EDCA, weuse AC-VI (video) for video transmission and AC-BE (besteffort) for http traffic transmission. In 802.11e HCCA, thevideo is delivered by scheduled polling and the http traffic isdelivered using EDCA-BE.Two traffic scenarios are considered. In the first scenario,

each station in cell A has 2 video streams plus http traffic,which leads to a total load of (2×384+100)×6≈ 5.2Mbps incell A. This is a heavy traffic load for a 802.11b network. Fig.8 shows the cumulative distribution of the end-to-end delay ofvideo application by ReB, EDCA and HCCA. We see that ReBhas a similar delay performance as HCCA when no interferepresents (in Fig. 8(a) where K = 0), but it outperformsHCCA when interference is introduced (in Fig. 8(b) whereK = 3). The performance of HCCA is even worse than EDCAwhen more inter-cell interference presents (in 8(b)). The goodperformance of ReB and HCCA in single cell case is due to thesuccessful resource reservation, although in difference ways.However, HCCA is more susceptible to inter-cell interferencethan ReB as its reservation is achieved by polling which lacksmechanism to avoid collision between neighboring cells. Inmulti-cell case, such polling-based reservation is difficult toguarantee QoS due to the lack of inter-AP coordinations.EDCA has worse delay performance than ReB particular whenless interference presents (Fig. 8(a)) because it is a fullycontention-based access method without resource reservation.Next in Fig. 9 we study the moderate traffic load case. Every

station in cell A has 1 video stream plus http traffic, leading toa total traffic load of (384+100)×6 ≈ 2.9Mbps in cell A. Wesee that for both single cell and multi-cell cases, HCCA hasworse delay performance than ReB and EDCA. This may bedue to the fact that in HCCA data is allowed to transmit onlyin the scheduled service periods. The data that arrive out ofthe scheduler service periods has to wait until next scheduledservice period, which causes additional delay. Whereas, inReB and EDCA data can be served immediately after a shortbackoff waiting time. ReB has similar delay performance toEDCA in case of moderate to light traffic loads, and theirperformances both degrade with the increase of neighboringcells.Table II presents the average end-to-end delay, jitter and

data loss rate for the two traffic scenarios shown in Fig. 8and Fig. 9. Overall, for heavy traffic, ReB has similar QoSperformance as HCCA in single cell but outperforms HCCAand EDCA in multi-cells; whereas for light to moderate traffic,ReB has QoS performance comparable to EDCA in single cell

HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 341

0 50 100 150 200 2500

20

40

60

80

100

Time (second)

|Q1|

2 STAs in cell A 10 STAs in cell A 20 STAs in cell A 3 STAs in cell A1 STA in cell A

Stage 1 Stage 5Stage 4Stage 3Stage 2

Figure 7. Convergence speed of the control algorithm when the number of stations in cell A changes (K = 0).

0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

End−to−end delay (s)

CD

F

ReB

HCCA

EDCA−VI

(a) K = 0

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

End−to−end delay (s)

CD

F

ReB

HCCA

EDCA−VI

(b) K = 3

Figure 8. Cumulative distribution of end-to-end delay for cell A when K neighboring cells present (heavily loaded traffic).

0 2 4 6 8 100

0.2

0.4

0.6

0.8

1

End−to−end delay (s)

CD

F

ReB

HCCA

EDCA−VI

(a) K = 0

0 0.01 0.02 0.03 0.04 0.050

0.2

0.4

0.6

0.8

1

End−to−end delay (s)

CD

F

ReB

HCCA

EDCA−VI

(b) K = 3

Figure 9. Cumulative distribution of end-to-end delay for cell A when K neighboring cells present (lightly loaded traffic).

342 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 3, APRIL 2010

Table IIMEASURED QOS RESULTS AMONG MAC PROTOCOLS

Ave. end-to-end delay (s) Ave. jitter (s) Ave. data loss rateTraffic load EDCA HCCA ReB EDCA HCCA ReB EDCA HCCA ReB

HeavyK=0 0.1564 0.0346 0.0219 0.0052 0.0068 0.0034 0.06% 0.0% 0.0%K=1 1.0259 0.5596 0.1955 0.0081 0.0141 0.0067 12.7% 0.17% 0.05%K=3 3.4971 5.4800 2.1079 0.0169 0.0213 0.0125 28.4% 40.7% 19.4%

ModerateK=0 0.0026 0.0125 0.0033 0.0008 0.0048 0.0014 0.0% 0.0% 0.0%K=1 0.0068 0.0202 0.0076 0.0035 0.0073 0.0031 0.54% 0.11% 0.05%K=3 0.0345 0.1597 0.0214 0.0109 0.0222 0.0066 2.32% 1.36% 0.13%

as well as multi-cells, and they both outperform HCCA. Thegood performance of ReB is contributed by the combinationof reservation and contention in the access procedure, whichprovides a reasonable tradeoff between QoS capability andsystem flexibility/scalability.

VII. SUMMARY

This paper presents a new reservation based backoff method(ReB) towards QoS provisioning for video streaming in wire-less 802.11 home networks. The ReB is fundamentally dif-ferent from traditional random backoff methods as it achievesreservation by reusing a time slot in consecutive backoff cy-cles. This backoff method borrows the concept of R-ALOHA,but make several important modifications to both accommo-date the variable slot sizes in CSMA network and leverageits carrier sense capabilities. This new method combines theadvantages of both the reservation based methods and thecontention based accesses. Analytical and simulation resultsshow that this solution can remarkably improve the systemperformance in terms of network throughput and delay, henceenabling effective QoS support. An AIMD based algorithm isthen proposed to enable efficient resource allocation for videostreaming over wireless LANs.The surprising effectiveness of the ReB method in wireless

LAN environment leads us to believe similar methods may beused for multi-hop networks. In the future, we plan to extendthe model into multi-hop ad-hoc networks. In addition, wealso plan to investigate on the quantitative impact of channelerrors and carrier sense errors on the performance of the ReBmethod.

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HE et al.: A RESERVATION BASED BACKOFF METHOD FOR VIDEO STREAMING IN 802.11 HOME NETWORKS 343

Yong He received the BS degree from TsinghuaUniversity, Beijing, China, in 2004. He is currentlyworking toward the PhD degree in the Center ForIntelligent and Networked Systems (CFINS), infor-mation school, Tsinghua University. His researchinterests include power management for wirelessnetworks, medium access control, and video stream-ing over wireless networks.

Jie Sun received her BS and MS degree from Xi’anJiaotong University, Xian, China, in 1999 and 2002respectively. She is currently working toward thePhD degree in the Department of Electrical andComputer Engineering, University of Massachusetts,Amherst. Her research interests cover analog equal-izer design for indoor transmission of Multi-Gigabit-Per-Second Data Rates Using Millimeter Waves.

Ruixi Yuan received the BS degree from the De-partment of Physics, University of Science andTechnology of China, in 1985, the MS degree fromthe Department of Physics, Texas A&M University,in 1986 and the PhD degree from the Departmentof Electrical Engineering, Texas A&M University,in 1991. He worked in the US technology indus-try before joining Tsinghua University as a fac-ulty member. His research interests include wirelesscommunication networks, complex system dynamicsand networked media. He is a member of the IEEE.

Weibo Gong (S’87-M’87-SM’97-F’99) received thePh.D. degree from Harvard University, Cambridge,MA, in 1987. He is with the Department of Electri-cal and Computer Engineering, University of Mas-sachusetts, Amherst, since 1987. He is also anAdjunct Professor in the Department of ComputerScience, University of Massachusetts. His major re-search interests include control and systems methodsin communication networks, network security, andnetwork modeling and analysis.Dr. Gong is a recipient of the IEEE Transactions

on Automatic Controls George Axelby Outstanding Paper Award. He is theProgram Committee Chair for the 43rd IEEE Conference on Decision andControl.