Improving Context Interpretation by Using Fuzzy Policies: The Case of Adaptive Video Streaming

Transcription

1 28th Symposium On Applied Computing Dependable and Adaptable Distributed Systems Track Improving Context Interpretation by Using Fuzzy Policies: The Case of Adaptive Video Streaming Lucas Provensi, Frank Eliassen, Roman Vitenberg and Romain Rouvoy 1

2 Adaptive Video Streaming Increasing number of application exploiting media streaming over the Internet: Video conferencing, video on demand, etc. Applications need to adapt to dynamic environments: Shared bottlenecks on the Internet 2

3 Challenges of Adaptation: Information Imperfection Video Capturing Application Raw Video File H264 Encoding RTSP Server network RTP Stream RTCP reports RTSP Client H264 Decoding Video Rendering Application Save to File Application requirements regarding video quality Encoder characteristics and parameters Signaling, streaming and feedback mechanisms Shared link dynamics 3

4 Existing Approaches Assume precise information Bitrate overshoots and frequent quality oscillations Examples: Increase/decrease protocols and TFRC Original video sequence Adapted using TCP-Friendly Rate Control 4

5 Proposed Solution Development approach for adaptive systems that: Captures imprecision by using fuzzy set theory Integrates a fuzzy inference engine into a modular MAPE-K adaptation loop 5

6 Proposed Solution Why fuzzy sets? Example: Reduce bitrate when packet loss fraction is high 1 Degree of confidence low acceptable high 0 2% 10% Packet Loss Fraction 6

7 Proposed Solution: MAPE-K loop H264 Encoding Actuator RTSP Server Video Stream RTP reports RTSP Client Sensor Knowledge Base RTCP reports Execute Domain specific ontologies Monitor Fuzzy Adaptation Policies events Plan Analyze 7

8 Proposed Solution: Knowledge Specification Domain specific ontology H264 Encoding Loss Event Actuator Event Low L.F. Loss Fraction Execute Acceptable L.F. RTSP Server Knowledge Base Domain specific ontologies sub-concept property fuzzy predicate Plan acceptable Video Stream RTP reports Fuzzy Adaptation Policies High L.F. Adaptation RTSP Client Policies Sensor RULE RULE 1 : : IF loss_fraction IS low THEN adjustment IS positive IF loss_fraction IS low AND rtt IS NOT high THEN adjustment IS positive WITH 0.4; RULE Monitor 2 : IF loss_fraction IS high THEN adjustment IS negative RULE 3 : IF loss_fraction IS acceptable THEN adjustment IS null Analyze RULE 4 : IF accumulated_loss IS negative AND rtt IS NOT high THEN adjustment IS positive WITH 0.2; low high 8

9 Proposed Solution: Analysis low acceptable H264 Encoding Actuator Execute RTSP Server Knowledge Base Video Stream Degree of truth RTP reports 0.1 high 0.4 RTSP Client Sensor Rule RULE 2 : IF loss_fraction IS high THEN adjustment IS negative Monitor RULE 3 : IF loss_fraction IS acceptable THEN adjustment IS null Measured Loss fraction Plan Fuzzy values for all possible actions Analyze Accumulation Rule Evaluation Fuzzification 9

10 Proposed Solution: Planning H264 Encoding Actuator RTSP Server Video Stream Rate Adjustment RTSP Client RTP reports Sensor Encoder adjustment Execute Knowledge Base negative null Monitor positive Adjustment crisp value Center of mass 1 Plan Analyze Defuzzification Fuzzy values for all possible actions 10

11 Evaluation: Network Level Simulations Fuzzy rate adaptation achieves better throughput, less oscillations and acceptable packet loss fraction 11

12 Evaluation: Video Quality Video Quality Metric Scenario 1 Scenario 2 Scenario 3 Fuzzy TFRC Fuzzy TFRC Fuzzy TFRC stssim (higher is better) Fuzzy Rate Adapt. TFRC Better overall video quality according to objective metrics 12

13 Conclusion Problem Adaptation in presence of imprecise information Our approach Separation of concerns Example: separate adaptation knowledge from its interpretation Integrate a fuzzy inference engine into the adaptation loop Validated for the case of adaptive video streaming Questions? 13

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15 Evaluation (extra) Definition of a complete set of adaptation rules implementing a PID bitrate controller ns-2 simulations Static topologies (dumbbell and parking lot) Varying number of competing flows (tcp and udp) Performance comparison with same application using TFRC protocol to determine the target bitrate Video quality evaluation Metrics: Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Spatial-Temporal SSIM (stssim), DCT based Video Quality Metric (VQM) 15

16 Evaluation (extra 2) Increasing number of tcp flows On/off traffic traversing a increasing number of intermediate routers 16

17 Evaluation (extra 3) Media Quality Evaluation Scenario 1: No competition; Scenario 2: Competing with a TCP flow (new reno) and a fixed bitrate UDP flow. Scenario 3: Competing with 10 TCP flows Metric Scenario 1 Scenario 2 Scenario 3 Fuzzy TFRC Fuzzy TFRC Fuzzy TFRC PSNR (higher is better) SSIM (higher is better) stssim (higher is better) VQM (lower is better)