Cross-Layer Optimization for State Update in Mobile Gaming
description
Transcript of Cross-Layer Optimization for State Update in Mobile Gaming
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
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ii Bb
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,...2,1ib
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Lagrangian Relaxation
λ: Lagrangian multiplier
})()({min)(11,...,, 21
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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