Proactive Prioritized Mixing of Scalable Video Packets in Push-Based Network Coding Overlays
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1 Proactive Prioritized Mixing of Scalable Video Packets in Push-Based Network Coding Overlays Michele Sanna and Ebroul Izquierdo School of Electronic Engineering and Computer Science Queen Mary, University of London, UK Correspondence: Packet Video Workshop 2013 December 12 and 13th San Jose, CA USA
2 Scalable video delivery Adaptation Adaptation Adaptation Streaming server(s) w. Content Delivery Network (P2P) Different receivers demands Unknown up/download bandwidths Slide 2
3 Slide 3 Scalable Compression H.264/SVC and H.265/SVC SVC Encoding SVC Encoded Bit-stream Mixed scalability Spatial scalability Temporal scalability Full Rate MPEG4 Part 16 AFX: Amd2 FAMC Stefanoski, Ostermann (2008)
4 Slide 4 Our Application End-system multicast node: 1. Data exchange
5 Our Application End-system multicast node: 1. Data exchange 2. Node status information Slide 5
6 Our Application End-system multicast node: 1. Data exchange 2. Node status information 3. Rate allocation Slide 6
7 Slide 7 Practical network coding Linear combination Transmission opportunity Async Rx Global coefficients Generation id Node Buffer Gaussian elimination N N ( 1) ( 1) ( 1) (0) j j j i i j 1 i 1 b' b c c c c Nbit block b GF(2 ) 1 1.5KBytes Nbit symbol c GF(2 ) m*8bits j Decoded video [Chou, Wu, 2003 ]
8 Slide 8 Practical network coding: scalable packet coding Packet of class 0 Packet of class l Packet full rate Decode classes from: N 0 pkts N 1 pkts N 2 pkts
9 Practical network coding: scalable packet coding Packet of class 0 Packet of class l Packet full rate Related work: Random coding, differentiate persistence: Stacked/progressive network codes [Lin, Li Liang, 2007] Multi-Generation Mixing [Halloush, Radha 2011] Expanding Windows [Sejdinovic, et. al. 2010] Slide 9
10 Practical network coding: scalable packet coding Packet of class 0 Packet of class l Packet full rate Related work: Rateless/fountain coding Raptor [Thomos, Frossard ] Scalable w. Expanding windows [Vukobratovic, et. al. 2009] Slide 10
11 Playback, Buffer Maps Playback buffer region Quality Layers GOP i - 1 GOP i GOP i + 1 GOP i + 2 GOP i + 3 GOP i + 4 Decoded Video Quality GOP i - 1 GOP i GOP i + 1 GOP i + 2 GOP i + 3 GOP i + 4 Node/chunk selection Info messages between nodes. Status of GOP at receiving node: (Partially or undecoded, Decoded, or Unwanted) Non-scalable case: random uniform peer/gop selection [R 2 - Wang, Li 2007] Scalable case: Layer selection and rate allocation Slide 11
12 Chunk/peer selection A conservative case: Send higher priority classes first -> uniform random choice Increase to enhancement layers after ACK Large braking overhead Rate Estimation: Find layer (or range of layers) with sustainable rate for receiver r, GOP g -> uniform random choice Estimate based on recent history Assumptions: senders may depart or free-ride but: 1 - observe the same receivers status 2 - can take the same decisions -> Independent Proactive Estimation Slide 12
13 Rate allocation 2 rate levels considered for decision: 1. Sustainable rate: Limitation all layers below this 2. Preliminary target rate: Pick the first layer below this and increase when lower layers are completed Estimation region Playback region GOP i -3 GOP i -2 GOP i -1 GOP i GOP i + 1 GOP i + 2 GOP i + 3 Buffer status of potential receiver r considered: Total Packet Rate (avg over estimation interval) Innovative Packet Rate (avg over estimation interval) Slide 13
14 Rate allocation Sustainable Rate (upper limit): l max = max l: N l < TPR x limitation factor Preliminary target rate (first pushed layer): l min = max l: N l < IPR x aggressiveness factor Slide 14
15 Simulation Results Simulation setup: Network simulator (ns2) Random 20 nodes stationary network Parameters Video: H.264/SVC mixed MGS/temporal scalability Rates: 190 kbps, 340 kbps, 750 kbps Playback region = estimation region 2 secs (8 GOPs) Performance measure Variable upload rate from nodes Estimation thresholds margins 30% Delivery efficiency (% GOPs delivered at a specific layer) Slide 15
16 Simulation Results Scalable adaptive delivery 5% off the non-scalable case Slide 16
17 Simulation Results Scalable adaptive delivery 5% off the non-scalable case 20% better than conservative method Slide 17
18 Slide 18 Concusion and future work Achievements: Presented a novel scalable video delivery network Proposed and proven validity of rate allocation algorithm Open issues: Overhead reduction: Braking Dynamics of new chunks/gops Adaptive estimation (learn from previous success)
19 Slide 19 Thank you! Q/A?
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