IEEE COMMUNICATIONS LETTERS, VOL. 13, NO. 6, JUNE 2009 441

Ant Colony Optimization Based Packet Scheduler forPeer-to-Peer Video Streaming

Young H. Jung, Student Member, IEEE, Hong-Sik Kim, Member, IEEE, and Yoonsik Choe, Member, IEEE

Abstract—This letter describes a new packet scheduling al-gorithm for enhancing the quality of distributed peer-to-peervideo streaming. The algorithm was designed for when streamingserver peers use error recovery such as automatic-repeat-request(ARQ) rather than error protection to avoid overburdening net-work resources. Ant colony optimization was used for schedulinggroups of packets to reflect the channel status and error recoveryeffect of multiple server peers heuristically. Simulation resultsshow that the proposed algorithm can enhance the quality ofdistributed video streaming services.

Index Terms—Distributed streaming, peer-to-peer streaming,ARQ, ant colony optimization.

I. INTRODUCTION

RECENTLY, there has been much interest in peer-to-peer (P2P) streaming that is based on distributed or

many-to-one transmission technology because of increasedbandwidth efficiency [1][2]. In the distributed streaming tech-nology, a client peer receives segments of stream data frommultiple server peers using independent connections. However,streaming data over multiple channels can also result inadditional delay for frame reassembly in accordance with thetransmission order of the data packets and the status of eachchannel.

Nguyen and Zakhor [3] proposed an optimal packet par-titioning algorithm (PPA) that can guarantee stream packetsnot to be duplicated, minimize the probability of packetsarriving late, and eventually minimize buffering delay at thereceiver in a distributed streaming system. This PPA is notsuitable for ARQ-based streaming systems because it doesnot consider the retransmission behavior of packets. Jung andChoe [4] proposed a model-based packet scheduler for dis-tributed P2P streaming systems with application layer ARQ.However, this algorithm is not always optimal because theactual packet transmission order is very unpredictable due tothe dynamics of retransmission in both the link and applicationlayers, especially when the service is used over a wirelessnetwork. Furthermore, because the scheduling of each packetcan influence the delivery time of previous and subsequentpackets, it is desirable to schedule a group of packets togetherto obtain a global optimal solution for the entire stream packetset. This letter describes a new packet scheduling algorithmfor ARQ-based P2P multimedia streaming to address thisproblem. We use Ant Colony Optimization (ACO) [5], a meta-heuristic combinatorial optimization technique to optimize thescheduling for a group of packets. More specifically, this

Manuscript received November 15, 2008. The associate editor coordinatingthe review of this letter and approving it for publication was H. Yousefi’zadeh.

The authors are with Yonsei University, Seoul, Korea (e-mail: {crosscom,hongsik, yschoe}@yonsei.ac.kr

Digital Object Identifier 10.1109/LCOMM.2009.081925

meta-heuristic optimization technique was used to reflect thecomplex behaviors of both application and link layer ARQ ina reinforcement learning method, because this is difficult toresolve with an exact closed-loop solution.

To the best of our knowledge, this is the first use ofan ACO algorithm in a scheduling algorithm for multiple-sender streaming. Simulation results show that the proposedscheduler can enhance the streaming quality of service byreducing frame loss and delay for frame reassembly. Theremainder of this letter is organized as follows. We definethe essential service system architecture and focus on theproblems of conventional schedulers in Section II. In SectionIII, we explain the proposed ACO-based scheduler, and wepresent the simulation results in Section IV. We conclude theletter in Section V.

II. SERVICE SYSTEM ARCHITECTURE AND PROBLEM

DEFINITION

Delay-constrained selective ARQ [6] is used for errorrecovery in video stream data. This includes playout buffer-ing, gap-based loss detection, and conditional retransmissionschemes. Playout buffering includes prior pre-roll bufferingto display the received video stream data, whereas gap-based loss detection refers to packet-loss detection based onthe gap between two successive transmitted pieces of data.Conditional retransmission refers to the intentional skippingof the retransmission of a lost packet if the lifetime of thepacket has expired. In addition, we assume that retransmissionpackets have a higher priority than normal scheduled packets;this simplifies both the evaluation and the simulation.

In this letter, we use the term delay for frame reassemblyto mean the time required for successful reassembly of oneframe using segmented packets from different server peers.Nguyen and Zakhor suggested their PPA to minimize thisframe reassembly delay in a distributed streaming system in anerror-free environment or in which forward error correction isused [3]. In other words, sender i that has a minimum expectedpacket arrival time Ai(n) in Eq.(1), should be chosen to sendthe nth packet of the streaming data:

Ai(n) = ni × σi + 2Di (1)

where ni is the number of packets already sent by senderi up to packet n, σi is the unit time to send one packetfor sender i, and Di is the estimated delay from sender ito the receiver. Using this PPA, a group of packets wereallocated to appropriate senders and each sender starts to sendallocated packets after receiving the corresponding controlsignal. However, if this scheduling is used in conjunctionwith ARQ which deploys gap-based loss detection, it will not

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442 IEEE COMMUNICATIONS LETTERS, VOL. 13, NO. 6, JUNE 2009

Fig. 1. Delay for frame reassembly.

Fig. 2. Graphical representation of the packet scheduling problem fordistributed streaming.

always produce the optimal scheduling solution. For example,assume two senders that have equal sending bandwidth (i.e.,BW=1) but quite different instantaneous loss rates (0% vs.50%). If one frame is composed of seven packets (sequenceID 1-7), the delay for frame reassembly in the case of PPAcan be seven time slots as shown in Fig. 1(a). However, if oneuses a packet schedule like that shown in Fig. 1(b) in the sameenvironment, the delay for frame reassembly can be reducedto five time slots.

III. ACO-BASED P2P STREAMING SCHEDULER

The proposed scheduling algorithm determines the senderof each packet in one group of pictures (GOP) using com-binatorial optimization. ACO uses a pheromone trail as thea posteriori probability and attractiveness as the a prioriprobability. In our scheduler design, the transmission delayfor whole frames in a GOP is exploited as a pheromoneand the transmission delay for each packet is exploited asattractiveness. Figure 2 shows a graphical representation of thepacket scheduling problem. In this figure, Sm(1 ≤ m ≤ M)means the server-peer identity among the total of M peersand Pn(1 ≤ n ≤ N) is the packet identity among the totalof N packets. During packet scheduling, we select N asthe total number of packets in each GOP. With this GOP-based packet scheduling, we can use the most recent channel

status of each server peer with the period of each GOP. Theterm, d(S(Pn−1, S(Pn), n) represents the arc that shows themovement of packet allocation to the server peer.

Step 1. Initialization: In this stage, initial pheromone valueτ0 is set to the average transmission time of whole packetswithout channel errors and packet retransmission.

Step 2. Solution Construction: Each ant constructs a solu-tion path, or allocates the sender peer for all packets accordingto this movement probability equation at the each state,

P (i, j, k, t|s) =[τ(i, j, k, t)]α[η(i, j, k|s)]β

Σl∈allowedi [τ(i, l, k, t)]α[η(i, l, k|s)]β(2)

where i and j represent the sender index to which the previouspacket(k − 1) and the current packet (k) are allocated. Inaddition, t is the iteration index, and s represents the currentlybuilt solution set. In Eq.(2), τ(i, j, k, t) is the pheromonetrail or the a posteriori probability that is updated in Step4. η(i, j, k|s) is the attractiveness, or the packet transmissiondelay when the current packet k will be sent by sender j alongwith scheduling path s.

Step 3. Cost Evaluation: If an ant completes a solutiontour, then it uses the cost function in Eq.(3) to determine ifthis solution path is the best so far.

f(s) = arg maxm∈M

((θm(σm + Dm) + Dm) × δm) (3)

where θm is the total number of packets allocated to senderm, and δm is the expected latency ratio. The expected latencyratio for each sender is calculated and updated heuristicallywhenever the client application receives streamed packets fromthe sender by observing the time difference between actualpacket arrival times, regardless of whether there has been aretransmission.

Step 4. Pheromone Update: After all ants end their tours,the pheromone trails of each arc are updated according toEq.(4).

τ(i, j, k, t) = (1 − ρ)τ(i, j, k, t − 1) + eΔτbs(i, j, k, t)+ ΣMAX ANT

l=1 Δτ l(i, j, k, t) (4)

where e is a parameter that defines the weight given to thebest tour so far, and τbs is the pheromone for that tour.

Step 5. Termination Test1: If K successive iterations of thisprocess result in no improvement, go to Step 6. Otherwise, goto Step 2.

Step 6. Termination Test2: If the current iteration numberof this process is greater than the maximum iteration numberT , stop the process. Otherwise, go to Step 1.

IV. SIMULATION RESULTS

To verify the performance of the proposed scheduler, weused the well-known trace-driven network simulator NS-2 [7].A 4-min MPEG-4-compressed video clip with a frame rate of30 fps was used for the simulations. The GOP size was set at60 frames (2 s). The average and peak bitrates of the videowere 532.833 kbps and 994.176 kbps, respectively. Streamingservice for three sender cases was simulated with this encodedbitstream. In the simulation, Sender1, Sender2, and Sender3had upload contribution bandwidths of 400, 250, and 150kbps and average packet loss rates of 0.02-0.15, 0.02, and

JUNG et al.: ANT COLONY OPTIMIZATION BASED PACKET SCHEDULER FOR PEER-TO-PEER VIDEO STREAMING 443

Fig. 3. Frame loss ratio over the variation of channel frame.

Fig. 4. Simulation result under different service-scales in an IEEE 802.11bnetwork.

0.01, respectively. We simulated four different systems for thesake of comparison: the proposed system with an ACO-basedpacket scheduler, a system with a channel-adaptive (CHA)scheduler [4], a system with PPA [3], and a simple round-robin(RR) scheduler. The total number of ants in the optimizationwas set to 30 and the maximum number of iterations to 300because these values produce a reasonable calculation time of900-1100 ms on an Intel Core 2 Duo Processor running theLinux operating system.

Table I shows simulation results when the packet lossrate in the channel from Sender1 is fixed at 0.08 underscenario1. In this table, AV G D, DEV D and MAX Dmean the average, standard deviation and maximum delayfor frame reassembly. In addition, FR LOSS means averageframe loss ratio as a quality measure. Whole factors of theproposed scheduler are significantly lower than those of theother schedulers. Because a shorter delay for frame reassemblycan produce more opportunities for ARQ, the client willexperience less frame loss. For that reason, the frame loss ratioof the proposed scheduler is also significantly lower. Fig. 3shows frame loss ratio as a function of the average loss rate inthe channel from Sender1. The proposed scheduler provides

TABLE ISIMULATION RESULT (BW1=400 KBPS, BW2=250 KBPS, BW3=150

KBPS, CH1=0.08, CH2=0.02, CH3=0.01)

AVG D DEV D MAX D FR LOSS(s) (s) (s) (%)

RR 0.516698 0.47471 3.527139 56.9861PPA 0.186162 0.259793 1.988608 8.4772CHA 0.124066 0.202912 1.976067 4.9722ACO 0.0929445 0.11536 0.944227 1.8333

much better streaming quality than the other schedulers forall packet loss rates. To generalize this result, we simulatedanother realistic service scenario: several clients and senderswere scattered within the rage of one 802.11b access-point.Fig. 4 shows the result of this simulation. In this figure,the frame loss ratio of RR scheduler was omitted becauseit averaged 54.39%, much larger than the others. As shown inthis result, the error recovery of the proposed scheduler alwaysoutperforms in all service-scale levels.

V. CONCLUSION

We have proposed an ACO-based packet scheduler fora retransmission-based distributed media streaming system.The proposed packet scheduling algorithm uses combina-torial optimization to schedule groups of multiple packetsand heuristic optimization to reflect the complex behaviorof application layer ARQ. Simulation results show that theproposed scheduling algorithm provides the shortest delay forframe reassembly and the lowest occurrence of frame losscompared to conventional distributed streaming schedulers.

REFERENCES

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