Impact of FEC Overhead on Scalable Video Streaming

1. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Impact of FEC Overhead on Scalable Video Streaming Seong-ryong Kang and Dmitri Loguinov Department of Computer Science Texas A&M University College Station, TX 77843 June 14, 2005 S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 1/30

2. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Outline Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 2/30

4. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

5. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

6. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work Internet streaming is an important part of the Internet S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

7. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work Internet streaming is an important part of the Internet Streaming applications usually require special mechanisms that can overcome packet loss without utilizing retransmission S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

8. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work Internet streaming is an important part of the Internet Streaming applications usually require special mechanisms that can overcome packet loss without utilizing retransmission FEC is often considered for recovering lost data segments S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

9. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work Internet streaming is an important part of the Internet Streaming applications usually require special mechanisms that can overcome packet loss without utilizing retransmission FEC is often considered for recovering lost data segments However, studies reported conﬂicting results on the beneﬁts of FEC S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

10. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Motivation of this work Internet streaming is an important part of the Internet Streaming applications usually require special mechanisms that can overcome packet loss without utilizing retransmission FEC is often considered for recovering lost data segments However, studies reported conflicting results on the benefits of FEC Our work aims to: address this uncertainty and provide additional insight into understanding how FEC overhead affects the performance of scalable video streaming S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 4/30

12. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Background S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 6/30

13. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Background FEC FEC schemes require application servers to send extra information along with the original data Media independent FEC Based on (N, k) block codes (such as parity or Reed-Solomon codes), where N is the size of an FEC block and k is the number of FEC packets in the block All data packets are recovered if the number of lost packets in a block is no more than k If more than k packets are lost, none of them can be recovered by the receiver S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 6/30

15. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Background FGS Streaming proﬁle of the ISO/IEC MPEG-4 standard Method of compressing residual video signal into a single enhancement layer Allows application servers to scale the enhancement layer to match variable network capacity during streaming The enhancement layer is typically coded at some ﬁxed bitrate and can be rescaled to any desired bitrate S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 7/30

17. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Background Because of dependency in the enhancement layer, higher sections of FGS cannot be used in decoding the frame without the presence of lower sections S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 8/30

18. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Background Because of dependency in loss the enhancement layer, higher sections of FGS cannot be used in decoding loss the frame without the useful presence of lower sections packets S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 8/30

20. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 10/30

21. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming We investigate the performance of FEC-based streaming considering Markov and renewal patterns of packet loss S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 10/30

22. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming We investigate the performance of FEC-based streaming considering Markov and renewal patterns of packet loss We use MPEG-4 FGS as an example and only examine the enhancement layer S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 10/30

23. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming We investigate the performance of FEC-based streaming considering Markov and renewal patterns of packet loss We use MPEG-4 FGS as an example and only examine the enhancement layer Note that many studies show that Internet packet loss can be captured by Markov models Alternating ON/OFF renewal process can model more general distribution of packet loss and allows heavy-tailed burst lengths S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 10/30

24. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming We investigate the performance of FEC-based streaming considering Markov and renewal patterns of packet loss We use MPEG-4 FGS as an example and only examine the enhancement layer Note that many studies show that Internet packet loss can be captured by Markov models Alternating ON/OFF renewal process can model more general distribution of packet loss and allows heavy-tailed burst lengths In what follows, we derive the expected amount of useful data E[Zj ] recovered from each frame Zj is the number of consecutively received packets in a frame j S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 10/30

26. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Assume that long-term network packet loss is given by p S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 11/30

27. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Assume that long-term network packet loss is given by p Loss process can be modeled by a two-state Markov chain: S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 11/30

28. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Assume that long-term network packet loss is given by p Loss process can be modeled by a two-state Markov chain: α 1—α 0 1 1—β β S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 11/30

29. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Assume that long-term network packet loss is given by p Loss process can be modeled by a two-state Markov chain: α 1—α 0 1 1—β β α and β are transition probabilities In the stationary state, probabilities π0 and π1 to ﬁnd the process in each of its two states are given by: β α π0 = , π1 = p = . (1) α+β α+β S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 11/30

31. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming To derive E[Zj ], we deﬁne: S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 12/30

32. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming To derive E[Zj ], we deﬁne: L to be the number of packets lost in an FEC block ¯ Q = E[Zj |L > k] to be the expected number of useful video packets recovered from the front of an FEC block when L > k S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 12/30

33. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming To derive E[Zj ], we deﬁne: L to be the number of packets lost in an FEC block ¯ Q = E[Zj |L > k] to be the expected number of useful video packets recovered from the front of an FEC block when L > k Then, we have the following result: S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 12/30

34. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming To derive E[Zj ], we deﬁne: L to be the number of packets lost in an FEC block ¯ Q = E[Zj |L > k] to be the expected number of useful video packets recovered from the front of an FEC block when L > k Then, we have the following result: Lemma 1 Assuming a two-state Markov packet loss and L > k, the expected number of useful video packets recovered per frame is: ¯ 1−p Q = E[Zj |L > k] = 1 − (1 − α)H , (2) α where H = N − k is the number of video packets in an FEC block S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 12/30

36. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Theorem 1 Assuming two-state Markov packet loss with average loss probability p, the expected number of useful packets recovered per FEC block of size N is: k E[Zj ] = P (N, i)H (3) i=0 N 1−p + P (N, i) 1 − (1 − α)H , α i=k+1 where P (N, i) is the probability of losing exactly i packets out of N transmitted packets. S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 13/30

37. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 14/30

38. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Deﬁne ψ to be the fraction of FEC packets in a block S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 14/30

39. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Deﬁne ψ to be the fraction of FEC packets in a block To verify the model, we simulate Markov loss process with two diﬀerent values of ψ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 14/30

40. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Deﬁne ψ to be the fraction of FEC packets in a block To verify the model, we simulate Markov loss process with two diﬀerent values of ψ 2500 350 2000 300 Decoding rate (kb/s) Decoding rate (kb/s) 250 1500 200 1000 150 Model Model 500 Simulation Simulation 100 500 1000 1500 2000 2500 3000 500 1000 1500 2000 2500 3000 Sending rate S (kb/s) Sending rate S (kb/s) (a) ψ = 1.1p (b) ψ = 0.9p ˜ Figure: Expected decoding rate R (p = 0.1, α = 0.08, and β = 0.72) S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 14/30

42. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the behavior of expected decoding rate changes for diﬀerent ψ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 15/30

43. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the behavior of expected decoding rate changes for diﬀerent ψ The amount of overhead in FEC-based streaming plays a signiﬁcant role in determining video quality S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 15/30

44. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the behavior of expected decoding rate changes for different ψ The amount of overhead in FEC-based streaming plays a significant role in determining video quality Next, we derive the utility of received video and examine how FEC overhead affects the quality of video S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 15/30

45. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the behavior of expected decoding rate changes for different ψ The amount of overhead in FEC-based streaming plays a significant role in determining video quality Next, we derive the utility of received video and examine how FEC overhead affects the quality of video Define the utility U as the fraction of received data that is useful for decoding S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 15/30

46. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Analysis of video streaming Theorem 2 Assuming Bernoulli packet loss in an FEC block of size N , average loss probability p, and FEC overhead rate ψ = ηp, (0 < ψ < 1), the utility of received video for each FEC block converges to the following as H → ∞:  0  0<η<1 lim U = 0.5 η=1 , (4) H→∞  1−ψ 1 < η < 1/p  1−p where η is constant. S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 16/30

48. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Simulation results under Bernoulli loss S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 17/30

49. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Simulation results under Bernoulli loss 1 0.8 η=1.1 0.6 η=1.0 Utility 0.4 Model η=0.9 Simulation 0.2 0 0 1000 2000 3000 4000 5000 Frame size H S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 17/30

50. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Simulation results under Bernoulli loss 1 0.8 η=1.1 0.6 η=1.0 Utility 0.4 Model η=0.9 Simulation 0.2 0 0 1000 2000 3000 4000 5000 Frame size H Note that U indeed converges to 0, 0.5 or (1 − ψ)/(1 − p) depending on the value of ψ as the streaming rate becomes large S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 17/30

52. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the asymptotic behavior of U in Theorem 2 holds for Markov and renewal patterns of packet loss S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 18/30

53. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the asymptotic behavior of U in Theorem 2 holds for Markov and renewal patterns of packet loss 1 1 h=1.2 0.8 0.8 0.6 h=1.0 0.6 Utility Utility 0.4 0.4 h = 1.2 h=0.8 Model h = 1.0 Simulation h = 0.8 0.2 0.2 0 0 500 1000 1500 500 1000 1500 Frame size H Frame size H (a) Markov loss (b) Renewal loss S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 18/30

54. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Simulation results Note that the asymptotic behavior of U in Theorem 2 holds for Markov and renewal patterns of packet loss 1 1 h=1.2 0.8 0.8 0.6 h=1.0 0.6 Utility Utility 0.4 0.4 h = 1.2 h=0.8 Model h = 1.0 Simulation h = 0.8 0.2 0.2 0 0 500 1000 1500 500 1000 1500 Frame size H Frame size H (a) Markov loss (b) Renewal loss Again, U exhibits percolation and converges to 0, 0.5 or (1 − ψ)/(1 − p) depending on ψ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 18/30

55. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Discussion S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 19/30

56. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Discussion Effectiveness of FEC depends on how the server uses redundant packets based on packet-loss dynamics Using fixed amount of overhead may cause significant quality degradation when packet loss fluctuates S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 19/30

57. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Discussion Simulation results of U for Effectiveness of FEC different packet loss p under depends on how the server improper amount of FEC overhead uses redundant packets based on packet-loss 1 dynamics 0.8 Using fixed amount of 0.6 Utility overhead may cause 0.4 ideal amount improper amount significant quality 0.2 degradation when packet 0 loss fluctuates 0 0.1 0.2 0.3 Packet loss 0.4 0.5 S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 19/30

59. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 21/30

60. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control Needs for adaptive control In a practical network environment, packet loss changes dynamically, depending on cross-traﬃc link quality routing updates, etc The amount of FEC needs to be adjusted according to changing packet loss to maintain high end-user utility S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 21/30

61. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 22/30

62. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control For adaptive FEC rate control, we use a simple proportional controller: S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 22/30

63. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control For adaptive FEC rate control, we use a simple proportional controller: ψi (n) = ψi (n − Di ) + τ (ηpi (n − Di ) − ψi (n − Di )) , (5) where index i represents flow number, pi (n) is the measured average packet loss in the FGS layer for flow i during interval n, τ is the controller’s gain parameter, Di is the round-trip delay for flow i. S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 22/30

64. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Adaptive FEC control For adaptive FEC rate control, we use a simple proportional controller: ψi (n) = ψi (n − Di ) + τ (ηpi (n − Di ) − ψi (n − Di )) , (5) where index i represents flow number, pi (n) is the measured average packet loss in the FGS layer for flow i during interval n, τ is the controller’s gain parameter, Di is the round-trip delay for flow i. Lemma 2 Controller (5) is stable if and only if 0 < τ < 2. S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 22/30

66. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Packet loss pattern S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 24/30

67. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Packet loss pattern We simulate a streaming session with a hypothetical packet loss pattern S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 24/30

68. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Packet loss pattern We simulate a streaming session with a hypothetical packet loss pattern 0.5 0.8 0.4 Transition probability 0.6 Packet loss 0.3 β 0.4 α 0.2 0.2 0.1 0 0 0 10 20 30 5 10 15 20 25 30 Time (seconds) Time (seconds) (a) Packet loss pattern (b) Transition probability α,β S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 24/30

69. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Achieved utility S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 25/30

70. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Achieved utility We investigate adaptive FEC overhead controller (5) with the behavior of achieved utility S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 25/30

71. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Achieved utility We investigate adaptive FEC overhead controller (5) with the behavior of achieved utility To illustrate the adaptivity of the controller, we use target utility UT = 0.8 S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 25/30

72. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Achieved utility We investigate adaptive FEC overhead controller (5) with the behavior of achieved utility To illustrate the adaptivity of the controller, we use target utility UT = 0.8 For comparison, we apply two diﬀerent scenarios that use ﬁxed amount of overhead S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 25/30

73. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Achieved utility We investigate adaptive FEC overhead controller (5) with the behavior of achieved utility To illustrate the adaptivity of the controller, we use target utility UT = 0.8 For comparison, we apply two different scenarios that use fixed amount of overhead The fixed-overhead amount is driven by the lower and upper bounds on packet loss p ˜ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 25/30

74. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion The evolution of achieved utility U S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 26/30

75. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion The evolution of achieved utility U adaptive adaptive 1 fixed 1 fixed 0.8 0.8 Utility Utility 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 10 20 30 0 10 20 30 Time (seconds) Time (seconds) (a) p = 0.1 ˜ (b) p = 0.4 ˜ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 26/30

76. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion The evolution of achieved utility U adaptive adaptive 1 fixed 1 fixed 0.8 0.8 Utility Utility 0.6 0.6 0.4 0.4 0.2 0.2 0 0 0 10 20 30 0 10 20 30 Time (seconds) Time (seconds) (a) p = 0.1 ˜ (b) p = 0.4 ˜ Our adaptive controller maintains the target utility UT very well along the entire streaming session However, ﬁxed-overhead schemes cannot maintain high utility as packet loss varies S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 26/30

77. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion PSNR quality S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 27/30

78. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion PSNR quality PSNR of CIF Foreman reconstructed with diﬀerent FEC overhead control S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 27/30

79. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion PSNR quality PSNR of CIF Foreman reconstructed with diﬀerent FEC overhead control M1 and M2 use ﬁxed amount of overhead driven by p = 0.1 ˜ and p = 0.4, respectively ˜ S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 27/30

80. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion PSNR quality PSNR of CIF Foreman reconstructed with diﬀerent FEC overhead control M1 and M2 use ﬁxed amount of overhead driven by p = 0.1 ˜ and p = 0.4, respectively ˜ 45 40 PSNR (dB) 35 adaptive 30 M1 M2 25 0 20 40 60 80 100 Frame number S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 27/30

81. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion PSNR quality PSNR of CIF Foreman reconstructed with different FEC overhead control M1 and M2 use fixed amount of overhead driven by p = 0.1 ˜ and p = 0.4, respectively ˜ The adaptive method offers 45 as much as 2.5 dB higher 40 PSNR than M2 for the first PSNR (dB) 60 frames 35 adaptive Also, outperforms M1 by 30 M1 M2 almost 10 dB for the last 40 25 0 20 40 60 80 100 frames Frame number S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 27/30

83. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Conclusion FEC has conflicting effects on video quality depending on the amount of overhead used Adaptive FEC overhead control can provide a high quality of video to end-users Proper control of FEC overhead can significantly improve the utility of received video over lossy channels S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 29/30

84. Motivation Background Impact of FEC on Scalable Video Adaptive FEC Control Evaluation Conclusion Thank you! Any questions? S. Kang and D. Loguinov Impact of FEC Overhead on Scalable Video Streaming 30/30

Impact of FEC Overhead on Scalable Video Streaming

Impact of FEC Overhead on Scalable Video Streaming

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Impact of FEC Overhead on Scalable Video Streaming