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Transmitting Scalable Video over a DiffServ network . EE368C Project Proposal Sangeun Han, Athina Markopoulou 1/30/01. References - Motivation. Scalable Video Coding & Transmission
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Transmitting Scalable Video over a DiffServ network EE368C Project Proposal Sangeun Han, Athina Markopoulou 1/30/01
References - Motivation • Scalable Video Coding & Transmission • U.Horn & B. Girod, “Scalable video transmission for the Internet”, Computer Networks and ISDN Systems, 1997. • M. van der Schaar & H.Radha, “A hybrid temporal-SNR FGS for the Internet video”, IEEE Trans. on Circuits and Systems for Video Technology. • J.Kimura & F.Tobagi, “Perceived quality and bandwidth characterization of layered MPEG-2 video encoding”, SPIE 1999. • S.McCanne, N.Vetterli & V.Jacobson, “Low complexity video coding for receiver -driven layered multicast”, JSAC 1997. • Differentiated Services • http://www.ietf.org/html-charters/diffserv-charter.html • RFC 2475, RFC 2597 • Software • NS simulator: http://www.isi.edu/nsnam/ns/ • ITU-T Recommendation H.263 (Annex O)
Overview • Scalable video coding: • MPEG-2, H.263: SNR, Spatial, Temporal, • MPEG-4, H.26L: FGS • Transmission over the network: • In general • DiffServ • Our scenario • Simulation setup • Issues
BW variation FGST FGST FGST FGST Fine-Granularity Scalability • Notes • Problems: limited scalability, error propagation • Standards: MPEG-4, H.26L • FGS advantages: transmission over networks w/ BW variation, error resilience
Network Server Receiver Transmission Loss • Small loss translates into drastic quality degradation(loss of important data + temporal dependence) • Transmission over the Internet is lossy • Need to use the available bandwidth to send the most important data
Solutions • Feedback + adapt transmission rate to variations • Disadvantages: complexity, granularity of BW adjustments, delay in feedback, overhead, inappropriate for multicast or high variability • Receiver Driven Layered Multicast • Problems: overhead, delay, granularity of BW • Smoothing + Admission control • Idea: limit stream and load variability. • Disadvantages: complexity, overhead, delay, model • Loss happens – control its effect by dealing with it intelligently: • Unequal error protection • Priority dropping Use the available bandwidth for the most important data
Drop prob High drop Low drop 1 Important Less Important 0 Buffer occupancy Priority Dropping • Loss is inevitable. Limit its effect when it happens. Prioritize information according to importance (contribution to quality) • Drop packets according to their priority Advantages: simple sender, handles heterogeneous receivers + short term congestion
2 1 2 Best Effort Integrated Services 3 Differentiated Services QoS architectures for the Internet • Best effort: no guarantees • Integrated Services (IntServ): Per-flow guarantees • Differentiated Services (DiffServ): Per aggregate guarantees
conditioning classification Example of a DiffServ node • Packet are marked (DSCP) • Each packet is treated according to this marking AF11
Drop prob AFx3 AFx2 AFx1 AFx1 AFx3 AFx2 AFx1 AFx3 1 0 Buffer occupancy AF class • IETF DiffServ WG in RFC 2597 defines 4 “Assured Forwarding” (AF) classes • Each AFx class: marking with AFx1, AFx2, AFx3 • Minimum BW guaranteed for the AFx aggregate. • 3 dropping priorities (1,2,3).
Scalable video in DiffServ • Use a particular AF Class for Video • Mark different layers with different AF dropping priorities • Define mechanisms to be used for the Video AF Class • Give rationale on how to create layers to work together with AF
Approach • Simulation & trying scenarios Playback buffer H.263+ Encoder + Layering RTP Packet. Depackt. Decoding + [Error Conceal.] DiffServ Network Marker (Packet Loss) Layered video Layered video RTP Transport
Issues • Purpose: show benefit of combining layering and priority dropping • Network point of view: Provide recommendations for DiffServ standardization: • How many priorities are really needed? • How to configure AF class? • How to choose the layering parameters? • Coding point of view: Explore benefit of layering/FGS: • under Internet loss scenarios • E.g., Tradeoff between motion smoothness and quality of pictures under buffer loss, or mix with bursty data • Significance of fine granularity for real scenarios