Presentation on theme: "Video Transmission Over Varying Bandwidth Links MTP Final Stage Presentation By: Laxmikant Patil Under Guidance of Prof. Sridhar Iyer."— Presentation transcript:
Video Transmission Over Varying Bandwidth Links MTP Final Stage Presentation By: Laxmikant Patil Under Guidance of Prof. Sridhar Iyer
Presentation Outline Introduction & Motivation Problem Definition Related Work Traffic Pattern based Adaptive Multimedia Multicast (TPAMM) Architecture Solution Strategy Simulation & Results Conclusion References
Introduction & Motivation Key Terms Playout Rate: The rate at which video is shown at client Delay Tolerant Applications: Clients can tolerate some delay before playout starts e.g. DEP offering live courses to remote students, Live concert streaming, MNCs training employees across cities Startup Latency: Maximum duration of time client is ready to wait before playout starts
Introduction & Motivation (Contd…) Need for Adaptive Mechanisms Heterogeneity of receivers capabilities oTransmission capabilities oDisplaying capabilities Heterogeneity of receivers requirements oDelay tolerance values oMinimum acceptable quality = 30 S R1 C1R2 C2C3 = 20 = 40 84 kbps 80 kbps 70 kbps 75 kbps 80 kbps
Introduction & Motivation (Contd…) 3 ways to transfer data from source to client 1.Streaming solution 2.Partial download 3.Complete download C S a i = Base encoding rate Time= L + Download duration Play S C Stream at rate a i a i is bottleneck b/w, Time= L S C Encoding rate a i = ? a i is avg. b/w for T X Time= L + startup_latency ?
Problem Definition “Objective is to use to overcome the problem of variations in link bandwidth and provide consistent video quality to the client.” We propose to use startup latency and prediction model based approach to overcome this
Example Given: Startup latency = 5 min Length of video L = 60 min a avg = ?
Related work [SAMM] Multilayering: Video is encoded as base layer and enhancement layers. Client receive number of layers depending on their capabilities Objective is to decide number of layers & encoding rates of each layer [KRTCR] Transcoding : Changes the encoding rate of the video file to desired rate Transcoding only at source Transcoding at relay nodes [AIMA] Buffer-based adaptation: uses occupancy of buffer on transmission path as a measure of congestion [AVMI] Simulcast: Source maintains different quality stream and receiver switches across streams. Combination of single-rate multicast and multiple-unicast.
TPAMM Architecture (Traffic Pattern based Adaptive Multimedia Multicast)
Solution strategy Single hop topology Multi hop topology Multicast tree topology Prediction window & offset computation
Prediction Window & Time-Offset Computation Startup latency Duration of video Encoding rate All predictions values per interval Prediction window We modify algorithm to work for prediction window size, by computing time-offset. Startup latency for next window = Current Startup latency + time-offset Duration of video for next window = Current duration of video - time-offset Last Prediction window
Prediction Window & Time-Offset Computation (Contd…) Prediction window Following values are known Encoding rate for current feedback interval (e.g. 60 kbps) Transmission rate for current feedback interval (e.g. 90 kbps) Feedback interval duration (e.g. 10 sec) Actual_playout_duration_Tx (A) is computed as (Encoding rate / Transmission rate ) * Feedback interval duration = 15 sec Expected_playout_duration_Tx (E) is computed as (current_playout_time) * Feedback interval duration = 10 sec (current_playout_time + current_startup_latency) Time-offset = (Actual_playout_duration_Tx) – (Expected_playout_duration_Tx) Time-offset for this example is 5 sec. Last Prediction window
Simulation & Results Effect of Delay Tolerance on Encoding Rate As Delay Tolerance increases Encoding Rate also increases
Simulation & Results (Contd…) Effect of Prediction Window size on Video Quality Parameter: Standard deviation of encoding rate As prediction window size increases, variations in video quality are reduced. With small increase in prediction window size, there is significant drop in variation.
Simulation & Results (Contd…) Effect of Prediction Window size on Video Quality As prediction window size increases, variations in video quality are reduced.
Simulation & Results (Contd…) Maximize Minimum Video Quality During Playout Minimum Video Quality throughout playout is maximized in TPAMM scheme.
Conclusion We have introduced a class of algorithms known as Traffic Pattern based Adaptive Multimedia Multicast (TPAMM) algorithms. In TPAMM scheme abrupt link bandwidth variations are not reflected at client side, ensuring good user perceived video quality. TPAMM scheme maximizes the minimum video quality during playout.
References 1.[SAMM] Brett Vickers, Albuquerque and Tatsuya Suda, Source- adaptive multi-layered multicast algorithm for real-time video distribution. IEEE/ACM Transactions on Networking, 8(6):720-733, 2000. 2.[AVMI] Jiangchuan Liu, Bo Li and Ya-Qin Zhang. Adaptive video multicast over the internet. IEEE Multimedia, 10(1):22-33,2003. 3.[KRTCR] Rajeev Kumar, JS Rao, AK Turuk, S. Chattopadhyay and GK Rao A protocol to support Qos for multimedia traffic over internet with transcoding www.ee.iastate.edu/~gmani/tiw- 2002/internet-qos.pdf 4.[AIMA] X. Wang and H. Schulzrinne. Comparison of adaptive internet multimedia applications. In IEICE Trans. COMMUN. 1999.
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