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A game attention model for efficient bit rate allocation in cloud gaming

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Abstract

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.

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Acknowledgments

We would like to give our special thanks to those who kindly participated in our subjective assessment. We also appreciate members of MPL and DISCOVER Labs for their helpful comments.

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Authors and Affiliations

  1. Multimedia Processing Laboratory (MPL), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Hamed Ahmadi, Saman Zad Tootaghaj, Mahmoud Reza Hashemi & Shervin Shirmohammadi

  2. Distributed and Collaborative Virtual Environments Research Laboratory (DISCOVER Lab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Shervin Shirmohammadi

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  1. Hamed Ahmadi
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  2. Saman Zad Tootaghaj
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  3. Mahmoud Reza Hashemi
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  4. Shervin Shirmohammadi
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Correspondence to Mahmoud Reza Hashemi.

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Ahmadi, H., Zad Tootaghaj, S., Hashemi, M.R. et al. A game attention model for efficient bit rate allocation in cloud gaming. Multimedia Systems 20, 485–501 (2014). https://doi.org/10.1007/s00530-014-0381-1

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