Video Streaming over Cognitive radio networks

1. Video Streaming over Cognitive radio networks CMPT 820 : Final Project Fall 2010 Presented by: AzinDastpak

2. Outline • Introduction • Problem Statement • Solution • Evaluation using Simulation • Conclusion and Future Works

3. Introduction • Current wireless networks are regulated by fixed spectrum assignment policy. Fixed Spectrum Assignment policy spectrum white spaces Inefficient spectrum utilization • Cognitive radio network is : • A new paradigm that provides the capability to share or use the spectrum in an opportunistic manner.

4. Cognitive Radio Network Components • Primary network • Primary users • Primary base station • Secondary network • Secondary users • Secondary base station • Spectrum broker • A scheduling server that shares the spectrum resources between different cognitive radio networks

5. Cognitive Radio Network Components Components of Cognitive Radio Network

6. Streaming Video over Cognitive Radio Network • Broadcast/Multicast Video over a Network • Primary user operates in a specified frequency band • Is the dedicated bandwidth enough for desired video quality Multimedia is one bandwidth-hungry application that can fully utilize the potentials of Cognitive Radio Networks.

7. Problem Statement • CRN co-exists with a Primary Network with channel bandwidth Cp • Secondary user has its own dedicated bandwidth • Access Rule for channel: TDMA • Video Encoding : FGS • Goal: Maximize perceived video Quality by users

8. Solution • FGS encoder, encodes the video file into two stream • Base layer • Enhancement layer • Quality refinement is proportional to the number of bits received • Base Layer frames delivery shall be guaranteed • Transmit base layer frames over the dedicated primary channel • Transmit Enhancement layer over Cognitive Radio Network • How many bits shall be allocated per frame? Secondary channel’s availability is dynamic. Need to predict future secondary channel states

9. Solution • Model the Primary Channel State with a two-state Markov Model • State A: Channel is Available • State B: Channel is Busy • Arrival and departure of the primary network as a continuous Poisson process with rate μ • Negatively Exponential Service time with mean time β = 1/μ.

10. Solution Two-state discrete Markov Chain Model

11. Solution • Estimate the average Available time (TA) and (TB) •  How many bits shall be allocated to each frame with the goal to  maximize the quality of perceived video overall the time period T = (TA) + (TB) • Multiplying this average time  by frame-rate we'll get the number of the frames that will be sent over the time that channel is available. • Considering the capacity of the available channel we have a bit budget • Allocate equal number of bits to each frame

12. Evaluation using Simulation • Implemented a discrete-event simulator in Java to simulate the behavior of Secondary channel according to Markov Chain Model. • Assumed the values of μ and λ to be 0.5 each. • The times of entering each state is inserted into a Calendar. • At the time of each event, the event will be extracted from the Calendar and average idle and busy times will be estimated.

13. Evaluation using Simulation • Compute the estimated average time: • avgIdletime = α(avgIdletime) + (1-α)(observedIdletime) • Allocate equal number of bits to each frame • At the end of each period of time (T = (TA) + (TB) ), we measure the number of lost frames.

14. Evaluation using Simulation Number of lost frames, estimating average time with different parameters

15. Evaluation using Simulation Number of lost frames, estimating average time with different parameters

16. Evaluation using Simulation Number of lost frames, estimating average time with different parameters

17. Conclusion • At the beginning of simulation the algorithm is still in initial state and number of lost frames is high or we are faced with channel underutilization. • Experimenting with different values for α, (α = 0.2) was more suitable to for estimation of each state’s time length, leading to less frame loss.

18. Future Work • Studying different real channels to assign close-to-real life values to channel characteristics • Assign optimal number of bits to each frame to minimize frame loss while maximizing the perceived quality over time T. • Secondary channel may follow a more complicated model, (HMM). • Try to observe the behavior of channels pick the most appropriate model.

19. References • [1] I. Akyildiz, W. Lee, M. Vuran, and S. Mohanty, “NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey,” Computer Networks, vol. 50, no. 13, pp. 2127–2159, 2006. • [2] Q. Zhao and B. Sadler, “A survey of dynamic spectrum access,” Signal Processing Magazine, IEEE, vol. 24, no. 3, pp.79–89, 2007. • [3] Q. Zhao, S. Geirhofer, L. Tong, and B. Sadler, “Opportunistic spectrum access via periodic channel sensing,” Signal Processing, IEEE Transactions on, vol. 56, no. 2, pp. 785–796, 2008. • [4] A. Fattahi, F. Fu, M. van derSchaar, and F. Paganini, “Mechanism-based resource allocation for multimedia transmission over spectrum agile wireless networks,” Selected Areas in Communications, IEEE Journal on, vol. 25, no. 3, pp. 601–612, 2007. • [5] H. Mansour, J. Huang, and V. Krishnamurthy, “Multi-user scalable video transmission control in cognitive radio networks as a Markovian dynamic game,” in Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. IEEE, 2010, pp. 4735–4740. • [6] D. Hu, S. Mao, Y. Hou, and J. Reed, “Scalable video multicast in cognitive radio networks,” Selected Areas in Communications, IEEE Journal on, vol. 28, no. 3, pp. 334–344, 2010.

20. Questions?