Cross-Layer Optimization for State Update in Mobile GamingYang Yu*, Zhu Li*, Larry Shi*, Yi-Chiun Chen+, Hua Xu+

*Application Research Center, Motorola Labs+Motorola Networks & Enterprise

Oct. 16 2007Wayne State University

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MotivationApplication trend: Large scale MMOG on mobile devices

Gaming requirements: Efficient state update is crucial for satisfactory gaming experienceNetwork constraints: Limited bandwidth, variable network delay and channel condition

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Problem Scenario

• Down-link state update from one WiMAX access point to all clients• Dead-reckoning algorithm for state update• Pre-specified bandwidth limitation• Real-time channel quality and network delay feedback

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Goal and Contributions• Goal: Minimize gaming state distortion with an efficient

state update mechanism that adapts to network states:– Limited bandwidth– Network delay

• Contributions:– Characterize the traffic-distortion tradeoffs of gaming behavior– Off-line optimization and a history-based prediction method for

on-line adaptation– Validation and evaluation using real game traces

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WiMAX Link Model0 1 2 3 4 5 6 7 8 …. N-1 0 1 …. M-1

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OFDM Symbol Number

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Dead-Reckoning Algorithm

Client A Client B

Client C

time

Predicted move @ B & C

Actual move @ A

Location update

Updates from A to server

timeUpdates from server to B & C

Distance difference >= δ

δ

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Traffic-Distortion Tradeoffs – Theoretical Intuition

Location function

Actual location

Estimated locationLocation difference

Update triggered when

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Traffic-Distortion Tradeoffs – Real Game Traces

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User Diversity

Fixed update threshold large variations in user distortion and update traffic

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Off-Line Problem Formulation•Assumption

–Game traces at the t-th second are known a priori•Given

–For all n clients, distortion function, Di(δi), and traffic function, Ri(δi),–The constellation size for each client i, αi, and OFDMA parameters, Q (frame rate) and h (number of sub-carriers per sub-channel)–Bandwidth constraint, B, in terms of total available clusters per frame

•Find –distortion threshold vector δ = {δ1, δ2, …, δn} and –cluster allocation vector b = {b1, b2, …, bn}, so as to minimize

•Subjec to

n

iiiD

1

)(

hbQR

iiii

)(

i

n

ii Bb

1

,...2,1ib

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Lagrangian Relaxation

λ: Lagrangian multiplier

})()({min)(11,...,, 21

n

i i

iin

iii

n

RDJ

})()({min)(i

iiiii

RDJi

i

separate

Time complexity: )loglog( nOΛ: domain of λΔ: domain of δ

λ = 0.08λ = 0.16

λ = 0.08λ = 0.16

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On-Line Adaptation

• Explore temporal locality of gaming behavior

• Historical data-based prediction

• Our simulation results show one second history performs the best for a driving game

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Evaluation Setup

• Baselines:– Off-line optimal allocation– Uniform policy: same bandwidth for all clients – Proportional policy: same δ for all clients bandwidth

allocation proportional to extent of state changes• Real 40 second traces for a driving game with 32

vehicles• Δ: [0.2, 10] meters• Update packet size: 200 bytes

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WiMAX Link Quality and Adaptive Coding

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Main Results

NABA performed close to Optimal Both NABA and Optimal were able to efficiently utilize the bandwidth constraint

200 total clusters (peak 4.3 Mbps), 10 ms network delay, 200 frames per second, 24 sub-carriers per sub-channel

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Impact of Bandwidth Constraint

•Distortion dropped with BW constraint•NABA approaches to Optimal

Both NABA and Optimal were able to efficiently utilize the bandwidth constraint

100 to 500 clusters (peak 2.2 – 10.8 Mbps)

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Impact of Network DelayNetwork delay: 10 – 100 ms

•Distortion increased with network delay•NABA performed close to Optimal

Both NABA and Optimal were able to efficiently utilize the bandwidth constraint

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Impact of Rounding

On average, <2% increase in distortion

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Related Works

• Study of the impact of network delay and packet loss on state consistency– Zhou 2004 (ACM trans. , Yasui 2005 (NetGames)

• Study of dead-reckoning– Suitability, Pantel 2002 (NetGames)– Accuracy, Aggarwal 2005 (NetGames)

Our paper is the first effort to model the traffic-distortion tradeoffs to facilitatebandwidth allocation in a wireless environment

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Conclusion• Revealed the traffic-distortion tradeoffs in dead-

reckoning algorithm

• Formulation & off-line optimization of the bandwidth allocation problem

• History-based prediction for on-line adaptation

• Validation and evaluation via real game traces

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Q & A