Bit allocation between texture and depth

• Bit allocation for multiview image compression using cubic synthesized view distortion model [1]
• Asymmetric coding of multi-view video plus depth based 3-D video for view rendering [2]
• View and rate scalable multiview image coding with depth-image-based rendering [3]
• Video and depth bitrate allocation in multiview compression [4]

References

[1] V. Velisavljevic, G. Cheung, and J. Chakareski, “Bit allocation for multiview image compression using cubic synthesized view distortion model,” in Proc. IEEE Int. Conf. Multimedia Expo., Jul. 2011, pp. 1–6.

[2] F. Shao, G. Jiang, M. Yu, K. Chen, and Y. S. Ho, “Asymmetric coding of multi-view video plus depth based 3-D video for view rendering,” IEEE Trans. Multimedia, vol. 14, no. 1, pp. 157–167, Feb. 2012.

[3] V. Velisavljevic, V. Stankovic, J. Chakareski, and G. Cheung, “View and rate scalable multiview image coding with depth-image-based rendering,” in Proc. IEEE Int. Conf. Digit. Signal Process., Jul. 2011, pp. 1–8.

[4]  K. Klimaszewski, K. Wegner, and M. Domanski, “Video and depth bitrate allocation in multiview compression,” in Proc. IEEE Int. Conf. Syst. Signals Image Process., May 2014, pp. 207–210.

How to compare two rate-distortion curves?

Rate-distortion (RD) curves are two-dimensional, therefore comparing them is not straightforward. Bjøntegaard Delta-rate (BD-rate) criterion [1] can be used to compare rate-distortion curves.


References
[1] G. Bjøntegaard. “Calculation of average PSNR differences between RD-curves”. ITU-T Video Coding Experts group document VCEG-M33, April. 2001.

What parameters affect the bitrate of a video?

• Encoding Settings
• Spatial Settings (such as resolution)
• Temporal Settings (such as frame rate)

should users receive the same bitrate or the same quality?

Since there is no linear correlation between the bitrate of a video stream and its perceptual quality [1], the fairness algorithm should assure that users get similar qualities rather than similar bitrates.



References  
[1] G. Cermak, M. Pinson, and S. Wolf. The relationship among video quality, screen resolution, and bit rate. Broadcasting, IEEE Transactions on, 57(2):258–262, 2011.

How to estimate achievable rate for each UE in LTE?

If gamma is the average signal-to-noise ratio (SNR) experienced by UE, the average rate per unit bandwidth is estimated as log2(1 + gamma) [1][2][3].
In fact, it is a simplified air interface model where the achievable rate for each UE is estimated according to the average channel state information (CSI) of its link.


References
[1] D. De Vleeschauwer, H. Viswanathan, A. Beck, S. Benno, G. Li, and R. Miller, “Optimization of HTTP adaptive streaming over mobile cellular networks,” in Proc. IEEE INFOCOM, 2013, pp. 898–997.
[2] A. E. Essaili, D. Schroeder, D. Staehle, M. Shehada, W. Kellerer, and E. Steinbach, “Quality-of-experience driven adaptive HTTP media delivery,” in Proc. IEEE Int. Conf. on Commun. (ICC), Budapest, Hungary, Jun 2013.
[3] Cicalo, Sergio, et al. "Improving QoE and Fairness in HTTP Adaptive Streaming over LTE Network." (2015).

Virtual View Distortion Models

You can find virtual view distortion models in these papers:

[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.
[2] Hamza, Ahmed, and Mohamed Hefeeda. "Adaptive streaming of interactive free viewpoint videos to heterogeneous clients." Proceedings of the 7th International Conference on Multimedia Systems. ACM, 2016.
[3] T.-Y. Chung, J.-Y. Sim, and C.-S. Kim. Bit allocation algorithm with novel view synthesis distortion model for multiview video plus depth coding. IEEE Transactions on Image Processing, 23(8):3254–3267, August 2014.
[4] V. Velisavljevi´c, G. Cheung, and J. Chakareski. Bit allocation for multiview image compression using cubic synthesized view distortion model. In Proc. of the IEEE International Conference on Multimedia and Expo, pages 1–6, July 2011.

Recommended bitrates for video streaming

Take a look at [1], [2] and [3]. 

References 
[1] http://stackoverflow.com/questions/24198739/what-bitrate-is-used-for-each-of-the-youtube-video-qualities-360p-1080p-in 
[2] https://support.google.com/youtube/answer/2853702?hl=en
[3] Mansy, Ahmed, Marwan Fayed, and Mostafa Ammar. "Network-layer fairness for adaptive video streams." IFIP Networking Conference (IFIP Networking), 2015. IEEE, 2015.

The optimal ratio between texture and depth

According to [1], the optimal ratio between texture and depth data remains the same for any total target bit-rate.


References
[1] E. Bosc, F. Racapé, V. Jantet, P. Riou, M. Pressigout, L. Morin, and V. Jantet. A study of depth/texture bit-rate allocation in multi-view video plus depth compression. annals of telecommunications - annales des télécommunications, pages 1–11, 2013.

How to avoid short-term bandwidth fluctuations?

In the process of estimating network bandwidth, the effect of short-term bandwidth fluctuations should be considered to avoid sudden sharp consequences.

In [1], they have used an exponential weighted moving average method.


References
[1] A. Hamza and M. Hefeeda. A DASH-based free-viewpoint video streaming system. In Proc. of the ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, pages 55–60, March 2014.

How to Solve a Decentralized POMDP

Solving a Dec-POMDP is a really challenging task. In fact, it is known that the problem of finding the optimal solution for a finite-horizon Dec-POMDP with even only two agents is NEXP-complete [1]. 

Therefore, so much effort has been spent by researchers during last decade to create efficient methods for finding exact or approximate solution of Dec-POMDP. [2] provides a recent survey of the existing methods.

In [3], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming. They have used Joint Equilibrium based Search for Policies (JESP) [4] to solve their Dec-POMDP model. JESP [4] is guaranteed to find a locally optimal joint policy. It relies on a procedure called alternating maximization, that computes a maximizing policy for one agent at a time, while keeping the policies of the other agents fixed.

Multi-Agent Decision Process (MADP) Toolbox [5], which provides software tools for modeling, specifying, planning and learning a variety of decision-theoretic problems in multi-agent systems. 



References
[1] D. S. Bernstein, S. Zilberstein, and N. Immerman, “The Complexity of Decentralized Control of Markov Decision Processes,” in Proc. Uncertainty in Artifical Intelligence, 2000, pp. 32–37.
[2] C. Amato, G. Chowdhary, A. Geramifard, N. K. Ure, and M. J. Kochenderfer, “Decentralized Control of Partially Observable Markov Decision Processes,” in Proc. IEEE CDC, 2013, pp. 2398–2405.
[3] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[4] R. Nair, M. Tambe, M. Yokoo, D. Pynadath, and S. Marsella, “Taming decentralized POMDPs: Towards efficient policy computation for multiagent settings,” in Proc. IJCAI, 2003, pp. 705–711.
[5] F. A. Oliehoek, M. T. J. Spaan, and P. Robbel, “MultiAgent Decision Process (MADP) Toolbox 0.3,” 2014.

Modeling the Internet Backbone Traffic

In [1], the probability distribution function of the cross-traffic for a backbone link has been considered as Gaussian. It is based on a study [2] on modeling the Internet backbone traffic at the flow level. The mean and variance of the rate of the cross-traffic are calculated in terms the average size and duration of the contributing flows.


References
[1] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.
[2] C. Barakat, P. Thiran, G. Iannaccone, C. Diot, and P. Owezarski, “Modeling Internet backbone traffic at the flow level,” IEEE Transactions on Signal Processing, vol. 51, no. 8, pp. 2111–2124, 2003.

MDP vs. POMDP

Markov Decision Process (MDP) [1] models decision problems under uncertainty when the full state information is available. In many real world problems this is not the case and only incomplete state information might be observable. Partially Observable Markov Decision Process (POMDP) [2] provides a powerful modeling framework for such problems. In multi-agent environments where there are several active decision-makers, Decentralized POMDP (Dec-POMDP) [3] is used. 

In [4], a decentralized version of POMDP (Dec-POMDP) has been used for rate-adaptive video streaming.

References
[1] D. P. Bertsekas, Dynamic programming and optimal control. Athena Scientific Belmont, MA, 1995, vol. I-II.
[2] L. P. Kaelbling, M. L. Littman, and A. R. Cassandra, “Planning and acting in partially observable stochastic domains,” Artificial Intelligence, vol. 101, no. 1-2, pp. 99–134, May 1998.

[3] F. a. Oliehoek, “Decentralized POMDPs,” in Reinforcement Learning: State-of-the-Art, M. Wiering and M. V. Otterlo, Eds. Springer, 2012, pp. 471–503.
[4] Hemmati, Mahdi, Abdulsalam Yassine, and Shervin Shirmohammadi. "A Dec-POMDP Model for Congestion Avoidance and Fair Allocation of Network Bandwidth in Rate-Adaptive Video Streaming." Computational Intelligence, 2015 IEEE Symposium Series on. IEEE, 2015.

A Unique Family of Functions for Fainess Measures

Fairness can have many different interpretations and criteria. Various fairness measures have been proposed across different scientific disciplines. 

Jain’s index [1] is very popular in network resource allocation.

It has been shown in [2] that many fairness measures can be explained via a single family function f_beta(.). For example, the Jain’s index corresponds to the special case of beta = -1.

References
[1] R. K. Jain, D.-M. W. Chiu, and W. R. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer System,” Tech. Rep., 1984.
[2] T. Lan, D. Kao, M. Chiang, A. Sabharwal, and M. Hiang, “An Axiomatic Theory of Fairness in Network Resource Allocation,” in Proc. IEEE INFOCOM, Mar. 2010, pp. 1–9.