A Distributed Mechanism for Dynamic ResourceTrading in Cooperative Mobile Video Streaming

Bandar Alqahtani∗, Lavy Libman∗,†, Salil Kanhere∗

∗ School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia† National ICT Australia, 13 Garden St, Eveleigh, NSW 2015, Australia

Email: bandara,llibman,salilk@cse.unsw.edu.au

Abstract—With the emergence of high speed mobile Internetaccess in smart devices, providing a specific quality of service(QoS) for video delivery is a challenging task due to the dynamicnature of the wireless channels. In this paper, we propose an auc-tion based mechanism that facilitates cooperation among mobilenodes, in order to increase the level of QoS satisfaction in thenetwork. In the proposed mechanism, users with excess resourcesthat are surplus to their QoS requirement (sellers) are motivatedto provide a relaying service to users with low QoS (buyers), inexchange for monetary compensation. Our mechanism is entirelydistributed and does not require a coordination by a centralizedauctioneer, which helps to minimize the implementation overheadand allows it to be applied regularly with short period times. Theefficiency of the auction mechanism is verified using simulationover a range of typical practical scenarios.

I. INTRODUCTION

Recent advances in smart phone and video applicationsas well as in wireless networking technologies are rapidlyopening up opportunities for media streaming over wire-less links. However, the volatile and time-varying nature ofwireless channels continues to present a major challenge forguaranteeing the quality of service (QoS) level of mediaapplications [1]. The common approach to deal with thisproblem is adaptive video streaming, which varies the bitrateof the video according to the real-time conditions of thewireless channel [2]. Although adaptive streaming ensures thatthe video is received smoothly within the prescribed delaybounds, it does so by sacrificing the quality of the video whenthe channel quality is low.

Recently, cooperative communications have emerged as apromising technique to exploit user diversity and providesignificant gains in reliability and capacity of wireless net-works [3], where intermediate relay nodes aid transmissionfrom the source to destination node [4]. Many studies havelooked at the benefits of cooperation in the context of videostreaming in mobile networks, showing it to be an effectivetechnique to improve the end-user QoS, either from theperspective of energy minimization [5] or throughput perfor-mance [6]. Most such works use a centralized mechanismfor the allocation of relay nodes to the video streams. Heand Guan [7] propose a distributed algorithm with multiple‘helpers’ (relays) each forwarding a part of the video tothe destination. Their algorithm focuses on maximizing the

†NICTA is funded by the Australian Department of Communicationsand the Australian Research Council through the ICT Centre of Excellenceprogram.

streaming capacity using a ‘greedy’ optimization of the linkrates, and cannot be applied in a dynamic network withchanging user population and volatile wireless channels. Wuet al [8] focus on scalable video streaming in the presence ofa cooperative relay, proposing a resource allocation algorithmthat considers jointly the channel conditions of the relay andthe video frame significance. However, their model is limited(consisting of only one relay) and does not account for apossible minimum QoS requirement for the user.

In general, distributed approaches for optimizing coopera-tive video streaming performance typically give rise to game-theoretic modeling; for example, Al-Kanj et al [9] study acoalitional game model for self-organizing of a number ofmobile nodes that are interested in the same content, whichthereby sidesteps the question of cooperation incentive orcompensation for relays that serve content to other nodes.When the video streams required by individual nodes are notrelated, an allocation mechanism that takes into account therelay compensation, such as an auction, is required [10]–[13].In [10], the authors propose an iterative auction mechanism,where the users iteratively update their bids to maximize theirown utility, given the knowledge of others’ previous bids.A pricing-based auction is proposed in [11] to motivate thecooperation by payment to the relay node. A heuristic relayallocation algorithm is proposed in [12], based on matchingtheory, with the objective of maximizing the sum throughputof the relays, rather than QoS satisfaction of individual users.From an auction-theoretic perspective, the most detailed anal-ysis of auction properties for relay allocation in cooperativecommunications can be found in [13]; that study focuseson a centralized single-shot double-auction mechanism (i.e.where an auctioneer mediates between a set of buyers anda set of sellers), formally showing the desirable properties oftruthful bidding and budget balance. However, that mechanismsacrifices the system efficiency, which means the users do notreceive an optimal data rate or QoS guarantee.

For the most part, the above studies focus on designingcentralized auction mechanisms, involving an entity that servesas an auctioneer coordinating the resource allocation based onthe buyers’ bids and the payment to the sellers. However, inthe context of cooperative video streaming in mobile networks,the channel quality can change quickly and, therefore, themechanism to match between cooperative relays and theirbeneficiaries must be lean enough to be used very frequently,potentially up to once in each super-frame. Due to their

Fig. 1: Cooperative video streaming network configuration.

associated coordination overheads, multi-stage centralized auc-tions are not suitable for this scenario. This fact motivatesus to design a distributed auction mechanism scheme, wherethe sellers announce the availability of spare capacity, andthe buyers select their preferred seller(s) (cooperative relays)directly, without further coordination by a third party.

Our contributions are as follows. We describe a game theo-retic framework for mobile-to-mobile cooperation to enhancethe overall level of video QoS satisfaction, and define therespective game formulation and utility functions based onAmplify-and-Forward (AF) relaying [4], though our ideas canbe readily adapted to other cooperative relaying methods. Wedevelop a distributed relay allocation algorithm that matchesusers with extra unneeded resources (e.g. bandwidth or timeslots), i.e. sellers, to serve as cooperative relays for theirpeers with an unsatisfactory QoS level, i.e. buyers, basedon compensation payments (in a virtual currency) to providethe incentive for cooperative relays. The distributed, low-overhead algorithm uses an iterative auction method whereusers announce their spare resources, and the price for thecooperative service of each node is adjusted according to thelevel of demand reported directly by the buyers.

The rest of the paper is organized as follows. Section IIexplains the details of our system model and assumptions.Section III discusses the strategy considerations of the playersand describes the details of the proposed algorithm. Weevaluate the proposed approach through extensive simulationsin Section IV. Finally, Section V concludes this paper.

II. SYSTEM MODEL

A. System Description

We consider a single-cell wireless network with multiplemobile users (MUs). Each MU wants to receive a differentvideo content from the base station (BS) as shown in Fig. 1.We assume that all the MUs in the network are capableof transmitting, receiving, or relaying an adaptive video bit-stream. The MUs are assumed to be selfish users that try tomaximize their utilities, defined further below. We assumethere are L MUs in the network, and denote the index setof the MUs by L = 1, 2, ..., L. Each MUk, k ∈ L has a

Fig. 2: Timing of the cooperation in the network.

minimum expected quality requirement for the received video,denoted by Qmin

k .Due to the volatility of wireless channels (caused by effects

such as shadowing or fading), the channel condition betweenthe BS and the MUs, i.e. the signal to noise ratio (SNR), mayfluctuate frequently. At any given time, some MUs may havea bad channel condition that is unable to meet their minimumexpected QoS, while some other MUs may be enjoying a verystrong channel that is surplus to their maximum requirement.Thus, there is an imbalance of satisfaction between the MUsin the network. Accordingly, we divide the MUs into twodifferent groups: the satisfied MUs, whose QoS requirementis met and who are able to offer their spare resources (e.g.,power or time-slots) to cooperatively assist other MUs; andthe non-satisfied MUs, that cannot meet the minimum QoSrequirement without assistance from other MUs. We denote theset of satisfied MUs by LSAT, containing a total of LSAT MUs,individually denoted by MUSAT

i . Similarly, we denote the setof non-satisfied MUs by LNST, with LNST MUs individuallydenoted by MUNST

j . It should be emphasized that these setsdo not remain fixed for any substantial length of time: eachMU places itself into one of these sets according to the stateof its channel, and, in principle, can switch roles after everysuper-frame.

B. Relay Utilization Details

Following the auction process described later in Sec-tion III-B, each MUSAT

i may be matched to serve as a relayto a unique MUNST

j , and grant a portion of its time-slot inexchange for a monetary compensation. Specifically, MUNST

j

will have its video relayed by MUSATi during a fraction

(1− ψTi,j) (0 ≤ (1− ψTi,j) ≤ 1) of a frame duration T , whilstpaying a price of θi,j (0 ≤ θi,j ≤ 1) defined as a fractionof a maximum budget of Gmax per frame (see Fig. 2). Wewill refer to θi,j and ψTi,j as the price and time-slot allocationnumbers respectively.

We assume that, in each frame of duration T , a fractionψTi,jT is first allocated for MUSAT

i to receive its own video.Then in the subsequent period of (1−ψTi,j)T , MUSAT

i uses acooperative Amplify-and-Forward method to relay the videopackets for MUNST

j . Then, MUNSTj applies maximum ratio

combining (MRC) [14] to the signal received from the BS inthe first ψTi,jT time-slot, and the signal received from MUSAT

i

in the subsequent (1− ψTi,j)T time-slot.

C. Link Transmission Rate

Assuming a standard Gaussian white-noise channel, thereceived signal at MUk using a sub-channel f written as

xRCVDk(f, n) =

√Pk(f)HBS

k (f)Bfk (n) + nk(f, n) (1)

where Pk(f) and HBSk (f) are the transmitted power and

propagation loss from the BS to the MUk on sub-channel f ,Bfk (n) is message symbol from the BS to the MUk at time n,

and nk(f, n) is the sampled thermal noise. The signal-to-noiseratio on the channel can be expressed by

SNRBSk (f) =

Pk(f)HBSk (f)

σ2(2)

where σ2 is the noise variance. Thus, following Shannon’scapacity formula, the maximum transmission rate for the directsignal from the BS to MUk is given by

CBSk (f) = F log2

(1 + SNRBS

k

)bits/sec (3)

where F is the bandwidth of sub-channel f . In terms of thevideo packet rate, for a MUSAT

i this can be written as

Qi(ψTi,j , f

)=

1

MψTi,jC

BSi (f), (4)

where the CBSi (f) is the transmission rate from the BS to

MU i, and M is the length of the video packets. For anunsatisfied MUNST

j receiving the assistance of an amplify-and-forward relay and using maximal-ratio combining, thepacket arrival rate can be written as

Qj(ψTi,j , f

)=

1

2M(1− ψTi,j)C

BS−Relayj (f), (5)

where CBS−Relayj (f) is the total data rate after cooperation,

given by

CBS−Relayj (f) = log2

(1 + SNRBS

i + SNRRelayj

)(6)

where SNRRelayj is the SNR corresponding to the amplified

signal retransmitted by the relay.

D. Utility Functions

As the satisfied users consume their own power to help theunsatisfied users in exchange for a monetary payment, it isappropriate to define the income of the satisfied users as theirutility. For simplicity, we assume that the satisfied users maketheir cooperative retransmissions with a fixed power level, andtherefore incur a fixed energy cost per transmission frame thatwe henceforth denote by Gmin (translated to monetary units).This is the minimal amount of compensation that must be paidto a satisfied MU before it can accept the role of a cooperativerelay. Thus, for MUSAT

i , the utility function is given by

USATi

(ψTi,j , f

)= θi,jGmax −Gmin. (7)

Likewise, for MUNSTj belonging to the non-satisfied set, the

utility function consists of the video quality received minusthe payment to the relay, as given by

UNSTj

(ψTi,j , f

)= Qj

(ψTi,j , f

)−θi,jGmax =

1

2M(1−ψTi,j)T

log2

(1 + SNRBS

k + SNRRelayj

)− θi,jGmax (8)

where it is implicit that the quality metric can be expressed inthe same utility units as the monetary payment.

III. COOPERATIVE RELAY ALLOCATION

A. User strategies

Our goal is to conduct a distributed relay allocation, givingrise to an auction setting in which each user selfishly aims tomaximize its own utility. In this auction, the sellers (satisfied

users) announce their willingness to serve as cooperative relaysfor a certain price, and the buyers (unsatisfied users) choosethe relay(s) from which to receive the service. We proceed toconsider the strategy considerations of both types of users.

For an unsatisfied user, given the announcements of all thesatisfied users within its range (which include the availabletime fraction (1−ψTi )T and the asking price θi, i ∈ LSAT),the best strategy is to bid for the relays that will bring itsown utility function to a maximum. Note that this implies thatthe unsatisfied user must be able to calculate the utility valuewith each potential relay. Thus, the unsatisfied user needs tobe able to assess the channel quality from each potential relay;this can be done by measuring the SNR of the messages thatcarry the price announcements from the satisfied users. Then,the optimal relay that should be chosen by the unsatisfied useris found by solving

arg maxi∈LSAT

UNSTj

(ψTi,j , f

)= arg max

i∈LSAT

1

2M(1− ψTi,j)T

log2

1 + SNRBSk +

∑ϑ∈Ωj

SNRMUϑj

− θi,jGmax (9)

It should be emphasized that, in determining its decisionusing the above expression, the unsatisfied user should firstremove from consideration those potential relays for whomthe available time fraction would not be sufficient to satisfythe user’s minimum quality requirement.

For a satisfied user MUSATi , the strategy choice consists of

the asking price for cooperation (posted in its announcementmessage). Recall that another component of that message is thefraction of time it can devote to cooperation with non-satisfiedusers; however, the latter is not part of the strategy the playercan choose, but is determined by its own link quality to theBS and the minimum video quality requirement, as follows:

ΨTi = M

Qmini

CBSi (f)

. (10)

The utility of the satisfied user will then depend on whetherthe offer is taken up by any willing nonsatisfied users; as inany market, if the asking price is set too high, no one mayaccept the offer. Clearly, in our scenario of a wireless networkwith varying-quality channels, the satisfied user is unable topredict in advance how the nonsatisfied buyers will respondto a particular price setting, as it cannot evaluate their utilityfunctions (even if the functions themselves were assumed tobe public, they depend on the current channel quality fromthe satsfied user, which would take a significant overhead forit to estimate). Due to this asymmetry of information, thesatisfied user is generally not in a position to compute its “best-response” strategy, hence studying the equilibrium propertiesof the game is not useful.

Instead, we consider a dynamic strategy (price) adjustmentmethod, where the satisfied user sets the price to some initialvalue and later adjusts it with fixed increments, until a balancebetween supply and demand is achieved. Specifically, after thesatisfied user announces the available amount of resource thatcan be devoted to cooperation and the asking price, each un-satisfied user MUNST

j responds by announcing which relay it

Algorithm 1 Initialization1: Input N,M,Gmax, C

BSk (f), CBS−Relay

k (f) , T,QminK

2: Output USATi

(ψT

i,j , f),UNST

j

(ψT

i,j , f)

3: if CBSk (f) ≥ Qmin

K then4: Add k to LSAT

5: else6: Add k to LNST

7: end if8: for all MUNST

j , j = 1, 2, . . . , LNST do9: Find all the neighboring MUSAT

i

10: for all MUSATi , i = 1, 2, . . . , LSAT do

11: Set t = 1, Qmini , and θi,j = 0

12: Define USATi

(ψT

i,j , f)

, i = 1, 2, . . . , LSAT

13: end for14: end for15: for all MUNST

j , j = 1, 2, . . . , LNST do16: Set t = 1, Qmin

j , Gmax

17: Define UNSTj

(ψT

i,j , f)

, j = 1, 2, . . . , LNST

18: end for

is willing to ‘buy’ from, and the minimum amount of resource(out of Ψi) that it needs to satisfy its video quality requirement.This is similar to the ‘tatonnement’ process commonly usedin microeconomic theory for modeling price adjustments ingeneral markets when the price-demand function is not knownin advance. Another analogy is that of an English auction,where the price starts from a low point and is increased untilonly one bidder (the winning bidder) remains; the notabledifference in our case is that multiple sellers compete for thebuyers’ attention at the same time.

As explained above, the satisfied user MUSATi is willing to

lease up to a total fraction of (1−Ψi) out of the frame durationT to the non-satisfied users. Hence, if MUSAT

i receivesrequests for cooperation from a group of MUNST

j , j ∈ LNSTRqs ,

with each MUNSTj requesting a minimum time fraction ΨT

j tosatisfy its quality requirement, then MUSAT

i must check thefollowing condition: ∑

j∈LNSTRqs

ΨTj ≤ 1−ΨT

i , (11)

which guarantees that the minimum required QoS of MUSATi

itself is not violated if it cooperatively serves all the requests.If the condition does not hold (which means that, at thecurrent asking price, the demand for cooperation by MUSAT

i

is too great), then it must reject the requests and may increasethe price in the next iteration of the auction. Once the totaldemand is no longer excessive (i.e., condition (11) is satisfied),MUSAT

i accepts the cooperation requests for the price offered.In the next subsection we elaborate in further detail the

auction algorithm, and subsequently evaluate the efficacy ofthe resulting relay assignment by simulation in Section IV.

B. Algorithm Description

The details of the proposed cooperative communication auc-tion algorithm are depicted in pseudo-code form in Algorithms1, 2, and 3. The proposed algorithm has 3 steps. Step 1 consistsof the algorithm initialization; specifically, in this step, theuser utilities and QoS of the received video for each user aredetermined, and the users are categorized into the satisfied andunsatisfied sets.

Step 2 is the main iteration of the algorithm. In this step,the satisfied users (the sellers) announce their cooperation

Algorithm 2 Price announcement and bidding procedure1: Input USAT

i

(ψT

i,j , f),UNST

j

(ψT

i,j , f)

2: Output F, S3: for all MUSAT

i do4: Each MUSAT

i calculates the amount of resource it wants to sell Ψi = 1 −

MQmin

iCBSi

(f)

5: Each MUSATi announces its start price θstarti,j to the neighboring MUNST

j ,j=1, 2, . . . , LNST

6: end for7: for all MUNST

j , j=1, 2, . . . , LNST do8: Each MUNST

j calculates the best i∗ = arg maxi∈LSATUNST

j

(ψT

i,j , f)

9: if θi,ti,j ≤ Gmax and UNSTj

(ψT

i,j , f)> 0 and QoSj ≥ Q

minj then

10: The MUNSTj bids for MUSAT

iBest , i.e., φi∗,j = 1

11: Set ψTi,j , j= 1, 2, . . . , LNST

12: end if13: end for14: Set F = φ1,j , . . . , φLSAT,j , . . . , φ1,LNST

, . . . , φLSAT,LNST

15: Set S = ψTi,1 , . . . , ψT

i,LNST

Algorithm 3 Auction winner determination and cooperation1: Input F, S2: Output Θ = θi,ti,j, i = 1, 2, . . . , LSAT, j = 1, 2, . . . , LNST

3: for all MUSATi i= 1, 2 , . . . , LSAT do

4: if∑

j∈LNST

(φi,j · ψT

i,j

)> 1−Ψi then

5: for all j = 1, 2, . . . , LNST do6: Set θi,t+1

i,j = θi,ti,j + ε7: φi,j = 08: end for9: else

10: θi,t+1i,j = θi,ti,j The θii,j and ψT

i,j are finalised; MUSATi does not

participate in any further iterations of Algorithm 2.11: end if12: end for13: if any MUNST

j remains unmatched with any MUSATi then

14: Set t = t+ 1 and repeat Algorithm 2; else end.15: end if

price, whereupon the unsatisfied users calculate their potentialutility values with each possible relay (based on the announcedprices) and make their optimal choice of relay to bid for.

In Step 3 of the algorithm, the satisfied users check the bidsreceived and decide whether condition (11) holds, i.e., whetherthe demand does not exceed the fraction of the frame durationthe satisfied user is willing to allocate for cooperation. Thosesatisfied users for whom the condition does not hold thenincrease the price (by a fixed increment, which is a parameterof the algorithm), and the algorithm then goes back to anotheriteration of Step 2. This process continues until the demandfor each satisfied user is not greater than its supply, and thealgorithm then terminates with the resulting matching betweensatisfied and unsatisfied users.

The above design, in which the sellers announce the pricebefore knowing the demand and subsequently adjust it, impliesthat there is an inherent asymmetry between the situation ofthe buyers and the sellers: when a buyer bids for a relay service(in response to an announcement), that buyer is committed tobuying the service for the announced price; on the other hand,when a seller announces its willingness to serve as cooperativerelay for a certain price, it cannot guarantee that the servicewill indeed be provided to every buyer that responds. Asexplained above, this asymmetry follows directly from thenature of the utility functions — more specifically, the factthat the buyers’ utility functions depend on the quality of thechannel between the relay and the buyer, while the sellers’(relays’) utilities do not.

The rationale of the above design, where the sellers transmit

their price announcements first, is that these transmissionsserve a dual purpose: they allow each unsatisfied user (poten-tial buyer) to estimate the current SNR on the channel fromeach seller, which is essential for it to compute its utility.Furthermore, the distributed nature of the auction does notrequire a pre-registration or other such advance discovery ofbuyers and sellers that are “within range” of each other. Rather,any unsatisfied user requiring assistance from a cooperativerelay can simply bid in response to any seller announcementmessage that it is able to receive. This allows new users thathappen to arrive in the system after the last auction instanceto immediately join the next auction without any overhead ofregistering with a centralized entity. As an added benefit, itenables non-satisfied users to take advantage opportunisticallyof any relay channels that happen to be in a good condition atthe moment, and not rely on coarse and imprecise estimatesbased, e.g., on distance from the relay node.

The initial asking price that is announced by each sellerin the first iteration of Step 2 should be set to at least theseller’s own cost of providing the relaying service. Moregenerally, for better efficiency it can be based on an “educatedguess” of the seller’s environment (e.g. the density and typicaldistances to other users).It is beyond the scope of this paper toconsider in greater detail how the initial asking price shouldbest be set. After the first iteration, however, any further priceincreases happen with a fixed increment, which is a parameterof the algorithm; in Section IV we explore how this incrementimpacts the speed of convergence and the overall outcome ofthe relay allocation.

Finally, we do not go into further detail about the specificnature of the virtual currency used to compensate the cooper-ative relays. In a similar fashion to well-known credit-basedincentive schemes used in the context of forwarding in ad hocnetworks, the virtual payment that a satisfied user receives canbe either used as credit, to be cashed later in the event thatits channel deteriorates and it requires cooperative assistancefrom another user. Alternatively it can take the form of a feediscount from the cellular operator, which has an interest toencourage cooperation among the users, allowing it to save onBS power costs while increasing the QoS satisfaction level ofits users. In this paper, we assume that the users are honestand do not consider the issue of payment enforcement after aseller agrees to serve as a cooperative relay; however, we pointout that, in general, the same enforcement schemes commonlyused in ad hoc networks (e.g. the cryptographic approach of[15]) can be applied in our setting as well.

IV. SIMULATION RESULTS

In this section, we investigate the performance of theproposed distributed algorithm via simulation. The networktopologies for our simulation scenarios are generated randomlyas follows. With the base station placed in the origin, we placeM satisfied users randomly along an inner quarter-circle at adistance of 80m, and N non-satisfied users randomly alongan outer quarter-circle at a distance of 100m from the origin.The channel quality between every pair of nodes is determinedaccording to the system model described in Section II, i.e.,based on path loss (with an exponent set to 4), white Gaussian

1 2 3 4 5 6 7 8 9 10180

190

200

210

220

230

240

250

260

Satisfied users power level [mW]

Ave

rag

e u

nsa

tisfie

d u

se

rs d

ata

ra

te (

Kb

ps)

Coop e rativ e scheme N = 4, M = 5

Coop e rativ e scheme N = 4, M = 8

No c oop e ration in the ne twork

Fig. 3: Average data rate of unsatisfied users (per user) vs. therelay power level.

2 3 4 5 6 7 8 9 10180

190

200

210

220

230

240

250

260

270

Number of satisfied users, M

Avera

ge u

nsatisfied u

sers

data

rate

(K

bps)

Coop e rativ e scheme N = 4 , P = 5mW

Coop e rativ e scheme N = 4 , P = 10mW

No coop e ration in the ne twork

Fig. 4: Average data rate of unsatisfied users (per user) vs. thenumber of satisfied users, for different relay power levels.

noise (with a thermal noise level of -110dBm), and randomRayleigh fading with long coherence time. The transmissionpower of the BS is set to 20W. We then set the QoS minimumrequirement of each user (Qmin

k ) according to the quality of itsdirect channel from the BS; specifically, the demand of eachof the satisfied users is set to be 50% of its channel capacity,while the demand of the non-satisfied users is double the directchannel capacity.

We demonstrate the throughput gain achieved with the relaymatching obtained by the proposed algorithm, in terms of thedata rate of the unsatisfied users, as well as the percentageof the (originally) unsatisfied users which now reach theirrequired QoS level with the cooperation. Figures 3 and 4illustrate the increase of the average data rate for N = 4unsatisfied users, as a function of the number of satisfied usersand their transmission power, respectively. Apart from thedramatic improvement relative to the direct (non-cooperative)base case, we observe that the data rate (and, therefore, theutility) of the unsatisfied users increases with the relay powerlevel, due to a higher overall SNR. It also increases with thenumber of satisfied users M , due to having a greater choiceof relays and a better chance for each unsatisfied node to beable to choose well-located relays nearby.

Figures 5 and 6 show similar curves, except the performanceon the vertical axis is measured in terms of the averagepercentage of non-satisfied users who are able to attain the

1 2 3 4 5 6 7 8 9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Satisfied users power level [mW]

Fra

ction o

f N

ST

achie

vin

g m

inim

um

QoS

Coop e rativ e scheme N = 4, M = 5

Coop e rativ e scheme N = 4, M = 8

Fig. 5: Fraction of NST achieving minimum QoS vs. the relaypower level.

2 3 4 5 6 7 8 9 100.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

Number of satisfied users, M

Fra

ctio

n o

f N

ST

ach

ievin

g m

inim

um

Qo

S

Coop e rativ e scheme N = 4 , P = 5mW

Coop e rativ e scheme N = 4 , P = 10mW

Fig. 6: Fraction of NST achieving minimum QoS vs. thenumber of satisfied users.

minimum QoS requirement with the help of cooperation.While the same trends can be observed, with respect to thedependence on the number of satisfied users (potential relays)and their power, we curiously note that the percentage doesnot approach 100% even when the relaying resources areabundant. This is explained by the fact that the users’ utilitiescontinue to increase in the data rate; hence, “fortunate” usersthat have a good channel from nearby relays will tend tocontinue bidding beyond the minimum amount of resourcesrequired to satisfy their minimum QoS level, and can “priceout” other unsatisfied users that are less well-located.

V. CONCLUSION

In this paper, we proposed a low overhead distributedauction scheme for improving mobile users video streamingQoS satisfaction level over mobile networks. The main ideafolds in enabiling mobile users (sellers) with a good channelfrom the BS and spare resources (time and power) to actas amplify-and-forward cooperative relays for other mobileusers (buyers), in exchange for payment in a virtual currency.Buyers bid for the relay(s) that offer the best utility in terms ofrelay channel quality and price. The scheme design does notrequire a centralized auctioneer, and can therefore be rerun

frequently to cater for the users’ mobility and their volatilechannels; furthermore, its opportunistic nature readily adaptsto changes in the user population (i.e. users joining or leavingthe network).

In this work, we have demonstrated the advantages and per-formance of the distributed auction scheme for relay selectionvia a simulation study of a mobile network. A theoreticalmodel of the auction mechanism, and a worst-case analysisof its performance compared to an optimal allocation ofrelays that would be achievable via centralized optimization,is currently the subject of ongoing work.

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