Cross-Layer Optimization for State Update in Mobile Gaming

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. 16 2007 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 August 8, 20142/21

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 August 8, 20143/21

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 August 8, 20144/21

5. WiMAX Link Model OFDM Symbol Number 8 … . … . 0 1 2 3 4 5 6 7 N-1 0 1 M-1 0 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 TTG Downlink Subframe Uplink Subframe Query Privacy in Wireless Sensor Networks August 8, 20145/21

6. Actual move @ A Predicted move @ B & C Distance difference >= δ Dead-Reckoning Algorithm Client B Client A Client C δ Location update time Updates from A to server time Updates from server to B & C Query Privacy in Wireless Sensor Networks August 8, 20146/21

7. Traffic-Distortion Tradeoffs – Theoretical Intuition Actual location Location function Estimated location Location difference Update triggered when Query Privacy in Wireless Sensor Networks August 8, 20147/21

8. Traffic-Distortion Tradeoffs – Real Game Traces Query Privacy in Wireless Sensor Networks August 8, 20148/21

9. User Diversity Fixed update threshold  large variations in user distortion and update traffic Query Privacy in Wireless Sensor Networks August 8, 20149/21

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 August 8, 201410/21

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 August 8, 201411/21

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 Query Privacy in Wireless Sensor Networks August 8, 201412/21

13. Evaluation Setup • Baselines: • Off-line optimal allocation • Uniform policy: same bandwidth for all clients • Proportional policy: same δfor 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 Query Privacy in Wireless Sensor Networks August 8, 201413/21

14. WiMAX Link Quality and Adaptive Coding Query Privacy in Wireless Sensor Networks August 8, 201414/21

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 Query Privacy in Wireless Sensor Networks August 8, 201415/21

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 August 8, 201416/21

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 August 8, 201417/21

18. Impact of Rounding On average, <2% increase in distortion Query Privacy in Wireless Sensor Networks August 8, 201418/21

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 August 8, 201419/21

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 August 8, 201420/21

21. Q & A Query Privacy in Wireless Sensor Networks August 8, 201421/21