Video Streaming Control by Predicting Stochastic Diffusion of TCP Throughput

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1 NEC HTTP/ ( ) 8% Video Streaming Control by Predicting Stochastic Diffusion of Throughput Hiroshi YOSHDIA, Kozo SATODA, and Satoshi NOGAKI Service Platforms Res. Labs., NEC Corp Shimonumabe, Nakahara-ku, Kawasaki, Japan h-yoshida@jh.jp.nec.com, k-satoda@cb.jp.nec.com, nogaki@ak.jp.nec.com Recently HTTP/ has been widely used for video streaming services over best-effort networks such as the Internet. However, the varying and insufficient throughput causes frequent playout interruptions. Concerning this issue, a number of adaptive video streaming methods have been researched and developed to adjust to the throughput fluctuations. These conventional methods do not achieve sufficient improvement yet because they adapt to the present throughput not to the future one. This paper proposes a novel adaptive streaming method that attains stable user experience by predicting stochastic diffusion of the future throughput. Experimental results show that a process model predicts the stochastic throughput diffusion with a high accuracy of over 8%. Furthermore, it is shown that the adaptive streaming method using the predicted stochastic throughput diffusion is able to suppress playout interruptions. 1. IP HTTP/ [1] HTTP Wang

2 2 [2] [1], [3], [4] ( ) (rate control) (adaptive streaming) [5] [7] [8] [5] [6] PID 1 [7] [8] Smooth Streaming Netflix Player Adobe OSMF [8] RTT [9] 2 (stochastic diffustion) 1 (Proportional) (Integral) (Derivative) 2 throughput [kbps] [9] ( ) [11] [14] History-Based(HB) Formula-Based(FB) [9], [1] HB

3 インターネットコンファレンス211 (IC211) 211年1月27日 - 1月28日 Auto-Regressive Moving Average(ARMA) (MMPP) probability density [11] Support Vector Regression(SVR) [12] FB [13] [14] HB FB FB 2 ( ) σ t 1/2 Zhang IP 1 ± x (t)( [15] x x + [15] (2) α x± (t) α=3 99.7% ( [ 3σ, 3σ] ) (2) x± (t) f (x, t) 2 1 (1) t= W (t) t σ2 (1) W (t) 2. 3 W (t) (2) N (, σ 2 t) (2) s, t > W (s + t) W (s) t= x± (t) µ (2) 2 N (, σ t) N (µt, σ 2 t) a) t x f (x, t) (1)! " (x x µt)2 1 f (x, t) = exp 2σ 2 t 2πσ 2 t x (2) x± (t) = x + µt ± ασ t 2. 2 (3) W () = ) HB (1) I(= 2)[sec] I[sec] 1 (1) σ2 t t G/3.5G Wi-Fi(Hotspot)

4 t=1 t=3 t=5 t=1 t= standard deviation experimental value theoretical value of model time unit (a) t=1 t=3 t=5 t=1 t= (b) accuracy (=1-RMS) accuracy (=1-RMS) error at each time unit time units error at each time unit 3 () 1 =3 19 2(/) 3 =114 b) () (2) σ µ σ n(= 3) (3) x( k) k x n σ 2 = 1 n 1 ( x x( k)) 2 (3) n 1 k= µ m(= 1) ( (4)) ( m 1 m 1 )( m 1 ) m ( k)x( k) ( k) x( k) µ = k= k 2 m 1 m k= k= ( m 1 ( k) k= k= ) 2 (4) 5 σ µ t x(t) z(t) = x(t) x µt σ a) t =, 1, 2,, 3 z(t) z(t) 3(a) ( t =1, 3, 5, 1, 3 ) 3(b) t c) (5) z(t)(t =, 1, 2,, 3) t ( 4 ) (2) z(t) t 4 t

5 stored video rate controller HTTP/ playout buffer playout controller Video Server IP Network Client (1) video bitrate (2) throughput (3) buffer length (4) playout rate 6 t z(t) t e(t) t e(k) (k =1, 2,,t) (RMS) 1 A(t) e(t) A(t) (6) (7) e(t) = z(t) t t (t>), e(t) = (t = ) (6) A(t) =1 t k= {e(k)}2 t +1 (7) 4 5 [,t] (t =, 1, 2,, 3) 8% t >1 87% ( ) ( ) % IP 3 (stored video) (rate controller) (3. 3) IP (IP network) HTTP/ (playout buffer) (playout controller) IP t[sec] (video bitrate) r(t)[bps] ( throughput) x(t)[bps] (buffer length) T p (t)[sec] [byte] [sec] (playout rate) p(t)[-] 1 p =2 1[s] 2[s] End-to-End [5] t[sec] t[sec] x(t) t[bits] r(t)[bps] x(t) t[bits]

6 (x(t) t)/r(t)[sec] p(t) t[sec] T p [sec] T p =(x t)/r p t t 2 [sec] 25[kbps] 35[kbps] 5[kbps] 1,5[kbps] 3 [sec] dt p dt = x r p (8) 3. 3 (2) x (t) x (t) α =3 [ 3σ, ] 99.87% x (t) T [sec] T p(t ) (8) T ( ) x T p(t )=T p() + r p dt (9) r p [,T] 3 ( x ) T p (T )=T p () + r p T + µ 2r T 2 2ασ 3r T 3 2 (1) T [sec] T p(t ) T r[sec] (11) K P r r + K P (T r T p (T )) (11) T [sec] T r [sec] 4. 7 (fragmented 3GP) 1 α 2 n 3 σ m 1 µ T 1 T [sec] T r 3 [sec] K P HTTP Servlet(Java) HTTP/ ( 3GP ) (2 ) (3 ) 4 25, 35, 5, 75, 1, [kbps] 7 3GP 3GP 2[sec] 15 ( 7) 1 3GP QuickTime Player(Apple) 3GP 2. 3 ( 8) 3 r [,T] (1) r t r (t)(t [,T]) [,T] 4 r r

7 1,6 [sec] (a) 1 (b) throughput [kbps] throughput [kbps] 1, , 1,5 1, 5 (a) 1 2 ( 9) x 3GP 1 (=2[sec] ) 1 5 (= ) (b) 2 8 ( ) ( ) 2 (9) x x x + µt (9) x x + µt 4. 2 a) ( ) [kbps] ( 1) 1 (a) 14[sec] (b) 2[sec] (a) 15[sec] (b) 3[sec] ( ) ( 11) x (t) σ t x (t) (a) 15 25[sec] (b) 2[sec] 5 I (Intra-coded Frame) I

8 1,6 1,2 8 4 interruption point , 1,5 1, 5 interruption point (a) 1 (b) 2 9 1,6 1,2 8 4 interruption point , 1,5 1, 5 interruption point (a) 1 (b) 2 1 1,6 1, , 1,5 1, (a) 1 (b) 2 11 ( )

9 2a ( 1) 522. [kbps] [kbps] 51.9 [kbps] [kbps] [kbps] [kbps] 59. [sec] 57.5 [sec]. [sec] 2b ( 2) [kbps] [kbps] 72.3 [kbps] [kbps] [kbps] [kbps] 56.6 [sec] 51.5 [sec]. [sec] (b) 2[sec] b) 2a 2b 2 2 1[kbps] x (t) ( 11(a)) 3[sec] (3[sec] ) 2 ( 9(a) 1(a)) 36[sec] (36[sec] ) 1[kbps] (1) 4 2ασ 3r T % [1] A. Zambelli, IIS Smooth Streaming Technical Overview, [2] B. Wang, J. Kurose, P. Shenoy, and D. Towsley, Multimedia Streaming via : An Analytic Performance Study, ACM Transactions on Multimedia Computing, Communications, and Applications, Vol. 4, Issue 2, May 28. [3] Adobe Systems Inc., HTTP Dynamic Streaming on the Adobe Flash Platform, products/httpdynamicstreaming/, 21. [4] Apple Inc., HTTP Live Streaming Overview, [5] IN28-21, Vol. 18, No. 458, pp , 29 3 [6] L. De Cicco, S. Mascolo, and V. Palmisano, Feedback Control for Adaptive Live Video Streaming, ACM Multi-

10 media Systems 11, Feb [7] X. Qiu, H. Liu, D. Li, S. Zhang, D. Ghosal, and B. Mukherjee, Optimizing HTTP-based Adaptive Video Streaming for Wireless Access Networks, IEEE International Conference on Broadband Network and Multimedia Technology 21, pp , Oct. 21. [8] S. Akhshabi, A. C. Begen, and C. Dovrolis, An Experimental Evaluation of Rate-Adaptation Algorithms in Adaptive Streaming over HTTP, ACM Multimedia Systems 11, Feb [9] Q. He, C. Dovrolis, and M. Ammar, On the Predictability of Large Transfer Throughput, ACM SIGCOMM 5, Vol. 35, Issue 4, pp , Oct. 25. [1] Q. He, C. Dovrolis, and M. Ammar, Prediction of throughput: formula-based and history-based methods, ACM SIGMETRICS 5, Vol. 33, Issue 1, pp , June 25. [11] A. Sang and S. Li, A Predictability Analysis of Network Traffic, INFOCOM 2, Vol. 1, pp , Mar. 2. [12] M. Mirza, J. Sommers, P. Barford, and X. Zhu, A Machine Learning Approach to Throughput Prediction, ACM SIGMETRICS 7, Vol. 35, Issue 1, pp , June 27. [13] M. Mathis, J. Semke, J. Mahdavi, and T. Ott, The Macroscopic Behavior of the Congestion Avoidance Algorithm, ACM Computer Communications Review, Vol. 27, Issue 3, pp , July [14] J. Padhye, V. Firoiu, D. Towsley, and J. Kurose, Modeling throughput: A Simple Model and its Empirical Validation, ACM SIGCOMM 98, Vol. 28, Issue 4, pp , Oct [15] T. Zhang, E. van den Berg, J. Chennikara, P. Agrawal, J. Chen, and T. Kodama, Local Predictive Resource Reservation for Handoff in Multimedia Wireless IP Networks, IEEE Journal on Selected Areas in Communications, Vol. 19, pp , Oct. 21.

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