1. Cross-Layer Optimization for State Update in Mobile Gaming
Yang Yu*, Zhu Li*, Larry Shi*, Yi-Chiun Chen+, Hua Xu+
*Application Research Center, Motorola Labs
+Motorola Networks & Enterprise
Oct
Wayne State University

2. Motivation Application trend: Large scale MMOG on mobile devices
Gaming requirements: Efficient state update is crucial for satisfactory gaming experience
Network constraints: Limited bandwidth, variable network delay and channel condition
Query Privacy in Wireless Sensor Networks November 15, /21

3. 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
Query Privacy in Wireless Sensor Networks November 15, /21

4. 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
Query Privacy in Wireless Sensor Networks November 15, /21

5. WiMAX Link Model OFDM Symbol Number FCH Burst Burst #4 #1 Burst #51 2 3 4 5 6 7 8 … .
N-1 1 … .
M-1 1
FCH
Burst 2
Burst #4 #1
ACK
Burst #5
Sub-Channel Logical Number DL
MAP
Preamble
Burst #6
Burst #2
CQI
Burst UL
MAP #7
Burst #3 Ns
Downlink Subframe
TTG
Uplink Subframe
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6. Dead-Reckoning Algorithm
Client A
Client B
Client C
Actual A δ
Predicted move
@ B & C
Location update
time
Updates from A to server
Updates from server to B & C
time
Distance difference >= δ
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7. Traffic-Distortion Tradeoffs – Theoretical Intuition
Actual location
Location function
Estimated location
Location difference
Update triggered when
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8. Traffic-Distortion Tradeoffs – Real Game Traces
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9. User Diversity
Fixed update threshold  large variations in user distortion and update traffic
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10. 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
Query Privacy in Wireless Sensor Networks November 15, /21

11. Lagrangian Relaxation
λ = 0.08
λ = 0.16
λ: Lagrangian multiplier
separate
λ = 0.08
λ = 0.16
Time complexity:
Λ: domain of λ
Δ: domain of δ
Query Privacy in Wireless Sensor Networks November 15, /21

12. 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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13. 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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14. WiMAX Link Quality and Adaptive Coding
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15. Main Results 200 total clusters (peak 4.3 Mbps), 10 ms network delay,
200 frames per second, 24 sub-carriers per sub-channel
NABA performed close to Optimal
Both NABA and Optimal were able to
efficiently utilize the bandwidth constraint
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16. Impact of Bandwidth Constraint
100 to 500 clusters (peak 2.2 – 10.8 Mbps)
Distortion dropped with BW constraint
NABA approaches to Optimal
Both NABA and Optimal were able to
efficiently utilize the bandwidth constraint
Query Privacy in Wireless Sensor Networks November 15, /21

17. Impact of Network Delay
Network 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
Query Privacy in Wireless Sensor Networks November 15, /21

18. Impact of Rounding On average, <2% increase in distortion
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19. 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 facilitate
bandwidth allocation in a wireless environment
Query Privacy in Wireless Sensor Networks November 15, /21

20. 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
Query Privacy in Wireless Sensor Networks November 15, /21

21. Q & A
Query Privacy in Wireless Sensor Networks November 15, /21