708 IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 4, APRIL 2014

Performance Evaluation ofIEEE 802.11p-Enabled Vehicular Video Surveillance System

Boris Bellalta, Evgeny Belyaev, Magnus Jonsson, and Alexey Vinel

Abstract—Prospective IEEE 802.11p-based vehicular surveil-lance system, where video from the vehicle on-board camera istransmitted to the management center, is considered. Multi-hoptransmission from the vehicle to the nearest roadside unit andthen – via other roadside units – to the gateway is addressed. Inthis letter we assess the feasibility of such system by analyzingthe video end-to-end distortion for a target vehicle, locatedseveral hops away from the gateway, when it is alone or thereare also other vehicles transmitting video. We demonstrate theimportance of dynamic adaptation of the video bit rate ofeach vehicle depending on the number and positions of theparticipating vehicles.

Index Terms—IEEE 802.11p, VANET, live video, multi-hop,surveillance.

I. INTRODUCTION

IEEE 802.11p in US and its European analogue ITS-G5specify a new communication technology for short-range

broadband connectivity between moving vehicles [1]. Vehicu-lar ad-hoc networks (VANETs) typically comprise of vehicularon-board units (OBUs) and fixed roadside units (RSUs).Availability of 802.11p-enabled communication channels al-low considering a variety of new road safety and infotainmentapplications including ones involving video transmission.

One of potentially promising applications is a vehicularvideo surveillance system aimed at delivery of video fromthe camera mounted on a specialized vehicle (public bus,police car, etc.) to the traffic management or emergencyservices center. The camera can be directed either outsidethe vehicle, e.g. to observe the current road conditions, orinside the vehicle, e.g. to prevent crimes and vandalism inpublic transportation. For such applications a dedicated IEEE802.11p infrastructure is an attractive solution for the policeand rescue services, since it allows avoiding the dependenceon public commercial cellular networks of mobile operators.

Brief summary of the recent relevant work is provided inTable I. [2] – [6] focus either on broadcasting of pre-encodedvideo from the server to the vehicles or interactive real-timeinter-vehicle video delivery. Heterogeneous media provisionin peer-to-peer-based vehicular networks is addressed in [7].Vehicle-to-infrastructure video delivery is studied in [8] with

Manuscript received January 27, 2014. The associate editor coordinatingthe review of this letter and approving it for publication was L. Zhou.

B. Bellalta is with Universitat Pompeu Fabra, Barcelona, Spain.E. Belyaev is with Tampere University of Technology, Finland.M. Jonsson and A. Vinel are with Halmstad University, Sweden (e-mail:

alexey.vinel@hh.se).This work was partly supported by the project of the National Natural

Science Foundation of China (NSFC) for International Young Scientists,COST Action IC0906, the Spanish Government under projects TEC2012-32354 (Plan Nacional I+D), and the Knowledge Foundation in Sweden.

Digital Object Identifier 10.1109/LCOMM.2014.022514.140206

TABLE IA SUMMARY OF A RELATED WORK

Scenario Video en-coding

Latency require-ments

References

”Broadcasting” fromserver to vehicles

Pre-encoded

Initial bufferinglatency 5-15seconds

[2], [3], [4],[7]

”Interactive” vehicle-to-vehicle transmission

Real-time End-to-endlatency ≤200 ms

[5], [6]

”Surveillance” vehicle-to-infrastructure trans-mission

Real-time End-to-endlatency ≤10 s

[8], thiswork

the focus on optimal handover decision-making when eachRSU is connected to a backbone. However, due to practicalrestrictions (e.g. high installation and deployment costs) itmight be that only some of the RSUs serve as gateways(GWs). The focus of our work is to understand the feasibilityof a network of RSUs when multi-hop forwarding of videotowards the nearest GW is required.

Video traffic delivery in VANETs is widely recognized as achallenging technical problem mostly due to limited capacityof the wireless channel, its error-prone nature and highlydynamic mobile environment. In addition, the use of IEEE802.11p Carrier Sense Multiple Access (CSMA) in multi-hopnetworks can create situations in which some nodes sufferfrom starvation to access the channel, becoming bottlenecksand causing severe packet losses. The later phenomenon, inapplication to live video streaming, is a subject matter of thisletter. Our contributions are summarized as follows:

• Multi-hop video transmission over a network with theIEEE 802.11p protocol is evaluated in terms of theachieved visual quality for a scalable extension ofthe H.264/AVC video coding standard (H.264/SVC) aswell as for a non standardized scalable video codecbased three-dimensional discrete wavelet transform (3-DDWT) [9].

• Feasibility of a network of RSUs located along the roadfor a video surveillance system is discussed. An adap-tive target video bit rate selection mechanism aiming atmaximizing achieved average video quality is proposed.

The letter is outlined as follows. System model assumptionsare introduced in Section II. Section III summarizes theapproach adopted for the analysis of throughput and packetlosses in multi-hop IEEE 802.11p networks, while Section IVexplains the video experimental setup used. Performance eval-uation results are presented in Section V.

1089-7798/14$31.00 c© 2014 IEEE

BELLALTA et al.: PERFORMANCE EVALUATION OF IEEE 802.11P-ENABLED VEHICULAR VIDEO SURVEILLANCE SYSTEM 709

��������

������������

������������

GW

OBU

MM−11 3

carriersenserange

2 4

RSU

ROAD

communicationrange

Fig. 1. The considered scenario.

II. SYSTEM MODEL

The following assumptions on the system operation areadopted in our study:

• We consider a specialized network consisting of M RSUsinstalled along the road, numbered from 1 to M , wherethe latest acts as a GW. The distance between adjacentRSUs coincides with their communication range.

• Since only a few vehicles follow each other with sched-uled time gaps in a public transportation case, there areN ≤ M vehicles, each one attached to a different RSU.

• Multi-channel operation [10] with two dedicated servicechannels (vehicle-RSU and RSU-RSU) with data rate Ris assumed. Each vehicle generates a video stream of rateRv, which is transmitted first to its associated RSU andthen via a sequence of RSUs to the GW.

• Packet transmission error probability is denoted as PER.The carrier-sense range is at least two times larger thanthe data communication range. Moreover, in case ofcollisions with hidden nodes, due to the capture effectit is possible to recover the strongest received packet.

• When the packet arrival rate to a RSU is higher than itspacket departure rate, we assume that the RSU drops thatexcess of arriving packets randomly. Note that the packetdeparture rate of each RSU depends on the number ofneighbors it has and their traffic load as well.

The described scenario is shown in Fig. 1. In this specificexample, there are three video flows directed to the GW fromthree vehicles attached to different RSUs. Note that RSU 3has to defer its transmissions whenever it detects the channeloccupied, which may happen when RSUs 1, 2, 4 or 5 aretransmitting. In case those RSUs are heavily loaded, RSU 3will suffer starvation, and it will have to drop most of itsincoming packets. Notice that, even if they belong to thesame video flow, packets transmitted by RSUs 1, 4 and 5are affecting the performance of RSU 3.

III. THROUGHPUT AND PACKET LOSS MODELS

To compute the throughput achieved by a video flow f , weuse the analytical approach presented in [11]. To simplify theanalysis, we consider that each video flow has an independenttransmitter at each RSU, which is called a virtual RSU. Eachvirtual RSU is equipped with an infinite buffer.

Since a flow traverses several virtual RSUs until it reachesthe GW, the traffic load at each hop depends on the amount oftraffic successfully transmitted over the previous hop. Let xf

i

be the throughput of virtual RSU f at RSU i, with f ∈ {1, N}

and i ∈ {1,M}. Then, the traffic load of virtual RSU f atRSU i + 1 is afi+1 = xf

i . Moreover, the traffic load of thefirst virtual RSU in the path of flow f is the video bit rategenerated by the video encoder, i.e., Rv , and the end-to-endthroughput of flow f is afM , i.e. the traffic that arrives to theRSU that acts as GW.

Let us define a network state S as the set of virtual RSUsthat are simultaneously transmitting, and φ the set of allnetwork states plus the state in which all virtual RSUs aresilent. The set φ forms a reversible Markov network, in whichdetailed balance between two adjacent states holds. From [11]the stationary probability for a state S is:

πS =

∏∀(f,i)∈S

ρfi θ

∑∀Z∈φ

∏∀(g,k)∈Z

ρgkθ, (1)

where ρfi is the probability that (f, i), i.e virtual RSU f atRSU i, has packets waiting for transmission in its buffer,and θ is the ratio between the expected packet transmissiontime and the expected backoff delay. The term ρfi θ modelsthe ‘aggressiveness’ of each virtual RSU in terms of thechannel utilization and is directly proportional to the numberof backlogged packets and their sizes, but inverse proportionalto the backoff waiting times.

The packet transmission time for an IEEE 802.11p wirelessnetwork is given by T = LH+L

R + SIFS + LACKR + DIFS,

where LH, L and LACK are the lengths of the header, payloadand acknowledgment, while SIFS and DIFS are the short anddistributed inter-frame spaces, respectively.

Then, the throughput achieved by flow f , or equivalentlyby virtual RSU f , at RSU i is given by

xfi = (1 − PER)

L

T

⎛⎝ ∑

∀S:(f,i)∈S

πS

⎞⎠ . (2)

To solve (1), we have to find the ρfi values that maximizethe throughput of each virtual RSU, subject to xf

i ≤ afi . Sinceafi depends on the throughput of previous virtual RSU in thepath of flow f to the GW, (1) and (2) form a non-linear systemof equations. To solve it, we combine a fixed-point iterationapproach with a gradient search algorithm. Further details canbe found in [11].

In those conditions, and assuming that all virtual RSUstraversed by flow f with a traffic load higher than thethroughput they can achieve, randomly drop the excess ofarriving packets, the probability to lose a packet is simply

710 IEEE COMMUNICATIONS LETTERS, VOL. 18, NO. 4, APRIL 2014

TABLE IIPACKET LOSS PROTECTION MODES

Protectionmode

1 2 3 ... 11 12

Protectionlevel

Four-foldrepetition

RS(6,2)

RS(7,3)

... RS(15,11)

No pro-tection

computed as the normalized difference between Rv and thereceived traffic at the gateway, i.e.

pfloss =Rv − afM

Rv. (3)

IV. EXPERIMENTAL SETUP

Two scalable video codecs using different compressionprinciples were used for performance evaluation: scalableextension of the H.264/AVC standard (or H.264/SVC) andscalable video codec based on three-dimensional discretewavelet transform (3-D DWT) [9], [14]. In case of H.264/SVCwe have used the JSVM 9.8 reference software [15] with twospatial and five temporal scalable layers. Group of pictures(GOP) size and intra-frame period was set to 16. For error-resilient coding we have used flexible macroblock orderingwith two slice groups and loss-aware rate-distortion optimizedmacroblock mode decision. A frame copy error concealmentmethod is used at the decoder side. In case of the 3-D DWTcodec the Haar transform in the temporal direction for GOPsize 16 and the 5/3 spatial wavelet transform at three levels ofthe decomposition were used. For error concealment the videobitstream of each wavelet subband is decoded in a progressiveway until a loss is detected.

In combination with unequal loss protection (ULP) ofdifferent video stream layers, scalable video coding providesrobust transmission over wireless channels with packet lossesand time-varying bandwidth. ULP can be implemented atthe application layer using inter-packet Reed-Solomon (RS)codes. In this case, the base video stream layer is protectedusing RS codes with a high redundancy level, while theremaining layers are protected with a lower redundancy levelor not protected at all. The list of used ULP levels isillustrated in Table II. Levels 2-11 are based on RS (n, k)code in finite field GF (28) with the generator polynomialg(x) = x4+30x3+216x2+231x+166, which forms n−k = 4parity packets for each k source packets. If k or more packetsfrom n are delivered, then the RS decoder is able to recoverall lost source packets.

For both codecs, simulation was done in the follow-ing way. For each target video bit rate value Rtarget

v ∈{250, 500, . . . , 5000} kbps, the packet loss probability plosswas calculated using (3), with the following IEEE 802.11pparameters: DIFS= 34 μs, SIFS= 16 μs, LH = 240 bits,LACK = 112 bits and R = 6 Mbps. A PER equal to 0.01 hasbeen chosen based on the results presented in [12]. The packetlength was set to L = 800 bytes. The Rtarget

v and ploss valuesas well as the test video sequence “Tampere 04“ (640× 480,300 frames, 30 Hz) [13], which is a typical video sequencefor road surveillance applications, were used as input for thevideo encoder. Then, video encoding parameters as well as

ULP parameters Ψ were jointly selected, such that{Ψ∗ = argmax

{Ψ}E[Y-PSNR(Ψ)]

Rv(Ψ∗) ≤ Rtarget

v ,(4)

where Rv(Ψ∗) is the video bit rate including source and parity

packets and E[Y-PSNR(Ψ)] is the Expected Peak Signal-to-Noise Ratio which we use as the visual quality metric.

For the E[Y-PSNR(Ψ)] calculation we have used the fol-lowing approach. First, a video codec generated the videostream as a set of source and parity packets. Then, werandomly removed the packets from this set by using theindependent packet losses model with loss probability ploss.The resulting video stream was used as an input for the videodecoder for reconstruction of the received video data. Afterthat the sum of square errors D between luma componentof original and reconstructed video was calculated. For eachvideo bit stream we have repeated that process K = 20 times,using different seeds for the random generator, and calculatedthe visual quality as E[Y-PSNR] = 10 log10

2552

1K

∑K−1i=0 Di

.

V. PERFORMANCE EVALUATION

We have performed our experiments with the two codecs asdescribed in Section IV and the packet loss model presentedin Section III. Figure 2 shows the expected visual quality fora scenario in which one vehicle attached to a RSU locatedseveral hops from the GW is transmitting video packets.Figure 2(a) shows the ploss and throughput depending onthe target video bit rate Rtarget

v , while Figure 2(b) shows thecorresponding maximum visual quality for each target videobit rate value. One can see, that in case of eight hops andone vehicle the best visual quality is achieved when the targetvideo bit rate is Rtarget

v = 1250 kbps. Herewith, the wrongselection of the video bit rate (25% more or less than theoptimal one) can result in a significant degradation of thevisual quality.

Using the approach described above we have found thebest visual quality value, and the target video bit rate atwhich it is achieved, when the vehicle is between 1 and 8hops to the gateway. Our results (see Table III) show thatin case there is only one vehicle transmitting video packets,excellent (> 36 dB) and good (32–36 dB) video qualitiescan be achieved. It can be observed that when the vehiclemoves away from the gateway, the best visual video quality isobtained by reducing the target video bit rate. The reason isthat due to the multi-hop packet transmissions, intermediateRSUs become overloaded and start to drop more and morevideo packets, which reduces the visual quality. Moreover, theresults show that the best target video bit rate depends alsoon the choice of video encoding algorithm. Note that an errorin the target video bit rate selection causes a higher qualitydegradation for H.264/SVC than for 3-D DWT (see Figure 2).

Tables IV and V show the visual quality achieved by thetarget vehicle placed at five and eight hops from the gateway,when there are also one and three other vehicles transmittingvideo packets to the gateway, respectively. For simplification,all vehicles are attached to different RSUs and transmit at thesame target video bit rate. One can observe that in most of thecases an acceptable (28–32 dB) visual quality is achieved by

BELLALTA et al.: PERFORMANCE EVALUATION OF IEEE 802.11P-ENABLED VEHICULAR VIDEO SURVEILLANCE SYSTEM 711

0 1000 2000 3000 4000 50000

175

350

525

700

875

1050

1225

1400

1575

1750

Thr

ough

put,

kbps

Target video bit rate, kbps0 1000 2000 3000 4000 5000

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Pac

ket l

oss

prob

abili

ty

(a) Throughput and packet loss probability

0 1000 2000 3000 4000 500024

26

28

30

32

34

36

Target video bit rate, kbps

E[Y

−P

SN

R],

dB

H.264/SVC (JSVM 9.8)3−D DWT

(b) Expected visual quality for different video codingschemes

Fig. 2. Results for one vehicle in eight hops from the gateway.TABLE III

BEST VISUAL QUALITY AND ITS CORRESPONDING TARGET VIDEO BIT RATE WHEN THERE IS A SINGLE VEHICLE IN THE SYSTEM

Hops to the gatewayCodec1 2 3 4 5 6 7 8

38.61 dB 35.32 dB 33.54 dB 33.51 dB 32.69 dB 32.67 dB 32.59 dB 32.59 dB3-D DWT5000 kbps 2500 kbps 1500 kbps 1500 kbps 1250 kbps 1250 kbps 1250 kbps 1250 kbps

41.40 dB 38.25 dB 36.08 dB 35.67 dB 34.92 dB 34.88 dB 34.81 dB 34.71 dBH.264/SVC4500 kbps 2250 kbps 1500 kbps 1500 kbps 1250 kbps 1250 kbps 1250 kbps 1250 kbps

TABLE IVBEST VISUAL QUALITY AND ITS CORRESPONDING TARGET VIDEO BIT

RATE FOR THE TARGET VEHICLE, PLACED AT FIVE HOPS TO THE

GATEWAY, DEPENDING ON THE POSITION OF THE SECOND VEHICLE

Hops to the gateway for the second vehicleCodec1 2 3 4

32.46 dB 31.75 dB 30.79 dB 30.79 dB3-D DWT1250 kbps 1000 kbps 750 kbps 750 kbps

33.92 dB 32.30 dB 32.30 dB 32.30 dBH.264/SVC1000 kbps 750 kbps 750 kbps 750 kbps

TABLE VBEST VISUAL QUALITY AND ITS CORRESPONDING TARGET VIDEO BIT

RATE FOR THE TARGET VEHICLE, PLACED AT EIGHT HOPS TO THE

GATEWAY, DEPENDING ON POSITION OF THE REST THREE VEHICLES

Hops to the gateway for the rest three vehiclesCodec7,6,5 6,5,4 6,4,2 5,3,1

28.30 dB 28.31 dB 29.11 dB 29.11 dB3-D DWT400 kbps 400 kbps 500 kbps 500 kbps

27.13 dB 27.78 dB 29.09 dB 29.09 dBH.264/SVC350 kbps 400 kbps 500 kbps 500 kbps

the target vehicle. In addition, it can be observed that when thenumber of active vehicles in the system increases, the videobit rate for every vehicle must be reduced in order to keep anacceptable video quality for all vehicles.

VI. CONCLUSION

In this paper, we have evaluated the feasibility of an IEEE802.11p vehicular video surveillance system, based on theexistence of a multi-hop roadside network, and proposed anadaptive target video bit rate selection mechanism for it. Thismechanism calculates the packet loss probability for a givenset of target video bit rates and then, it determines the besttarget video bit rate, which provides the maximum possiblevisual quality. We have shown that the best target bit video rateis highly dependent on the number of vehicles and their posi-tions as well as the video coding algorithm. Herewith, wrongselection of the channel rate (25% more or less with respect

to the optimal one) can result in a significant degradation ofthe visual quality. Based on the presented results, future workwill be dedicated to the development of a practical signalingprotocol, which would enable the proposed target video bitrate adaptation for vehicular surveillance system.

REFERENCES

[1] G. Karagiannis et al., “Vehicular networking: a survey and tutorial onrequirements, architectures, challenges, standards and solutions,” IEEECommun. Surveys and Tutorials, vol. 13, no. 4, pp. 584–616, 2011.

[2] F. Soldo et al., “Video streaming distribution in VANETs,” IEEE Trans.Parallel Distrib. Syst., vol. 22, no. 7, pp. 1085–1091, 2011.

[3] M. Xing and L. Cai, “Adaptive video streaming with inter-vehicle relayfor highway VANET scenario,” in Proc. 2012 IEEE ICC, pp. 5168–5172.

[4] M. Asefi et al., “A mobility-aware and quality-driven retransmissionlimit adaptation scheme for video streaming over VANETs,” IEEETrans. Wireless Commun., vol. 11, no. 5, pp. 1817–1827, 2012.

[5] F. Xie et al., “Performance study of live video streaming over highwayvehicular ad hoc networks,” in Proc. 2007 IEEE VTC — Fall, pp. 2121–2125.

[6] A. Vinel et al., “An overtaking assistance system based on jointbeaconing and real-time video transmission,” IEEE Trans. Veh. Technol.,vol. 61, no. 5, pp. 2319–2329, 2012.

[7] B. L. Zhou et al., “Distributed media-service scheme for P2P-basedvehicular networks,” IEEE Trans. Veh. Technol., vol. 60, no. 2, pp. 692–703, 2011.

[8] L. Zhu et al., “Cross-layer design for video transmissions in metropassenger information systems,” IEEE Trans. Veh. Technol., vol. 60,no. 3, pp. 1171–1181, 2011.

[9] E.Belyaev et al., “A low-complexity bit-plane entropy coding and ratecontrol for 3-D DWT based video coding,” IEEE Trans. Multimedia,vol. 15, no. 8, pp.1786–1799, 2013.

[10] C. Campolo and A. Molinaro, “Multichannel communications in vehic-ular ad-hoc networks: a survey,” IEEE Commun. Mag., vol. 51, no. 5,pp. 158–169, 2013.

[11] R. Laufer and L. Kleinrock, “On the capacity of wireless CSMA/CAmultihop networks,” 2013 IEEE INFOCOM.

[12] A. Bohm et al., “Evaluating CALM M5-based V2V communication invarious road settings through field trials,” IEEE LCN, 2010.

[13] http://www.cs.tut.fi/∼belyaev/v2v.htm.[14] E. Belyaev et al., “A low-complexity joint source-channel video coding

for 3-D DWT codec,” J. of Commun., vol. 8, no. 12, pp.893–901, 2013.[15] JSVM 9.8 software package, http://iphome.hhi.de/