[This is a short introduction pitch to the full paper.] Though there is an explosion of cloud services in recent years, cloud gaming industry is still way behind in making a strong breakthrough. Despite many benefits of cloud gaming (video or pixel streaming of rendered game scenes), the primary factor that hinders the growth is "response latency", a well known issue. In this paper, we introduce a novel predictive paradigm of cloud gaming in order to mitigate latency that is inherent in cloud gaming systems. The main idea of the predictive paradigm is to pre-generate and send future outcome frames in advance to the thin-client so that the thin-client can respond immediately to any user input. The approach in this work seeks to exploit the near unlimited processing power of the cloud and ever increasing network bandwidth. The paper provides a generic and comprehensive theoretical model that shows the relationship between various parameters (network bandwidth, round-trip-time and processing time for game logic, predicting future outcomes, rendering, capturing, encoding and decoding) of predictive cloud gaming and response latency. The theocratical model design is started with an ideal server with unlimited resources for ease of understanding and then it is enhanced with resource constraints of a practically feasible non-ideal server. The model demonstrates significant amount of response latency reduction in predictive cloud gaming when compared to conventional cloud gaming. The model is analysed further to estimate the resource requirements for predictive cloud gaming. A simple game is built to demonstrate key aspects of the model and to conduct subjective evaluation of quality of experience (QoE) of the game play. The evaluations show significant gain in QoE. The resource requirements of the predictive cloud gaming gaming system grows exponentially with respect to prediction period of future frames. The paper illustrates some optimisation methods to reduce exponential growth and discusses directions for further works in this area.

1. +
CloudHide: Towards Latency
Hiding Techniques for Thin-
client Cloud Gaming
Bhojan Anand, Pan Wenren
School of Computing
National University of Singapore
ACM Multimedia, 23 OCT 2017, Mountain View, SFO, USA

2. +
✔
Cloud
Server
Cloud Gaming
Render
Capture
Encode
Stream
Render
Decode
Simulate StateKey/Mse
Thin
Client
+ Quality:
Better Graphics, Physics and AI
+ Maintenance:
Easy Upgrades/Patches
+ Security
Piracy is hard
Cheating is hard (multiplayer games)
In-game purchase hacks/frauds are hard
-- Latency
Network
Encoding/decoding
VM processing
-- Scalability
Server bandwidth
Processing

3. + Parallel simulations of future
impulse events
Current State
Predicted States for Valid User Actions at the time
Possible
User Actions
Objective: Start the
response (eg. start a
jumping animation) to key-
press immediately.
Model with a simple 2-key (LEFT, RIGHT, NO-ACTION) game….
Client: Select frame
based on key-press.

4. +
Model with Ideal & Non-ideal Server
Ideal System
Tprocess << RTT
Ttransmit << RTT
eg. RTT :160ms
Frame time (FT) : 40 secs
Actions (A) : 3
Input Sampling rate: 40
89% response
delay
improvement
A0,A1,A2,A3
Frames
A4
A4
A4
Delayed local rendering…
- Reduces exp growth
- 1 FT: still 67% better
Handling NW Bandwidth:
- High similarity->high
compression ratio
Non-Ideal System
(server processing, client
processing, sever bandwidth)
Tprocess + Ttransmit <= FT
That is presented in our
paper. Let’s discuss!
Number Predicted Frames
• αRTT
• αNW-BW
• αGame Actions
• αProcessing
• α (1/latency-tolerance)

5. +
Thank you!
 FULL PAPER WILL BE AVAILABLE AT ACM DIGITAL LIBRARY
AFTER OCT 23RD, 2017.
Related Keywords:
Gamelets,
Superstreamer,
Cloudhide,
Cloud Game,
Procedural Content Generation,
Game AI,
Behaviour Modelling
Thank you!