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Adaptive Content-Aware Scaling to Enhance Video Streaming Performance

This paper discusses the development and implementation of a Content-Aware Scaling (CAS) system aimed at improving video streaming for multimedia applications. Given the predominance of multimedia traffic on the internet, traditional protocols like TCP prove insufficient for smooth delivery of content. We propose a methodology that incorporates motion measurement mechanisms to determine appropriate scaling strategies—temporal, quality, and spatial—based on content analysis. Through rigorous experiments and a user study, we demonstrate how CAS can adaptively reduce data rates while preserving video quality, effectively addressing bandwidth challenges in video streaming.

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Adaptive Content-Aware Scaling to Enhance Video Streaming Performance

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  1. Adaptive Content-Aware Scaling for Improved Video Streaming. Avanish Tripathi Advisor: Mark Claypool Reader: Bob Kinicki

  2. Outline • Introduction • Motivation • Related Work • Methodology • Experiments • Results • Conclusions and Future Work

  3. Motivation • Internet disseminates enormous amounts of information • TCP is the de facto standard… but • TCP is not ideal for multimedia and… • 77% of all Web traffic is Multimedia, of this about 33% is streaming content. [Chandra, Ellis ’99]

  4. Multimedia Flows… • …tend to use UDP with no congestion control • Other network protocols are being developed: • TFRC: smooth reduction in rates as against abrupt drops in TCP [Floyd et. al. ’00] • RAP: Architecture for delivery of layered encoded streams. [Rejaie et. al. ’99] • MPEG-TFRCP: Mapping MPEG to TFRC Protocol [Miyabayashi et. al. ’00] • Idea-rate based with smooth increase and decrease

  5. Multimedia issues • Generally very high bandwidth requirements • Random packet drop by routers during congestion is detrimental to perceptual quality due to interdependencies between packets • Need application level solution…

  6. …Media Scaling • Need media scaling: Application level data-rate reduction • Scaling types: • Temporal • Quality • Spatial • “Content of the stream should influence the choice of scaling mechanism” To the best of our knowledge this idea has not yet been employed

  7. Related Work • Quality Scaling: Receiver-driven Layered Multicast [McCanne ’96] • Temporal Scaling: • Player for adaptive MPEG Streaming [Walpole et. al. ‘97] • Better Behaved Better Performing MM networking [Chung, Claypool ‘00] • Content based forwarding for differentiated networks: use priorities based on MPEG characteristics [Shin et. al. ’00] • Filtering System: used for media scaling of MPEG streams. [Yeadon ’96]

  8. Outline • Introduction • Motivation • Related Work • Methodology • Experiments • Results • Conclusions and Future Work

  9. Methodology: Content-Aware Scaling • Develop and verify motion measurement mechanism • Define temporal and quality scaling levels • Evaluate the potential impact of content-aware scaling • Build system to do content-aware scaling adaptively • Evaluate the practical impact of the full system

  10. MPEG Overview • Three kinds of pictures • I- Intra encoding • P- Predictive encoding • B- Bi-directional predictive encoding • Subdivided into Macroblocks • Intra, predictive, interpolated macroblocks • Motion vectors are used for motion compensation

  11. Motion Measurement • Higher percentage of interpolated macroblocks means low motion • Lower percentage of interpolated macroblocks means high motion • Conducted a pilot study to verify our hypothesis • Divide frame into 16 sub-blocks • Count the number of blocks that have motion • Correlate that with the percentage of Interpolated macroblocks.

  12. Pilot Study Result: Motion Measurement

  13. Motion Computation • Keep latency low so that the system is sufficiently reactive

  14. Methodology: Content-Aware Scaling • Develop and verify motion measurement mechanism • Define temporal and quality scaling levels • Evaluate the potential impact of content-aware scaling • Build system to do content-aware scaling adaptively • Evaluate the practical impact of the full system

  15. Filtering • We extend the system developed at Lancaster university • Frame dropping filter (Temporal Scaling) • Requantization filter (Quality Scaling)

  16. User Study Details • 22 graduate and undergraduate students in the department • Platform: • 3 Pentium III machines with 128MB RAM running Linux • Clips were on local hard drives • Four ~10 second clips (2 high motion, 2 low motion) • Users rated the clips with numbers from 0 -100

  17. User Study Details • Five versions of each clip: Perfect, Temporal Level 1, Temporal Level 2, Quality Level 1, Quality Level 2

  18. Methodology: Content-Aware Scaling • Develop and verify motion measurement mechanism • Define temporal and quality scaling levels • Evaluate the potential impact of content-aware scaling • Build system to do content-aware scaling adaptively • Evaluate the practical impact of the full system

  19. Results • Four men sitting at a bar • Low Motion ( 70 % interpolated macroblocks)

  20. Results • A girl walks across a room while talking on the phone • Low Motion (57% interpolated Macroblocks)

  21. Results • Rodeo scene where a man on horseback tries to rope a bull • High Motion (27% interpolated macroblocks)

  22. Results • Car commerical • High Motion (20% interpolated macroblocks)

  23. Methodology: Content-Aware Scaling • Develop and verify motion measurement mechanism • Define temporal and quality scaling levels • Evaluate the potential impact of content-aware scaling • Build system to do content-aware scaling adaptively • Evaluate the practical impact of the full system

  24. Full System Architecture Quality Filter Internet High Motion Measurement MPEG Feedback Generator Server Client Input Low Temporal Filter

  25. System Functionality • Server is capable of quantifying motion as the movie plays • The filtering system has five scale levels for finer granularity • The system is adaptive and scales movies in real-time depending on the loss pattern as received from the feedback module

  26. User Study • Four clips (2 or more scene) ~30 seconds • Four versions of each • Perfect Quality • Temporal scaling • Quality scaling • Adaptive scaling • Bandwidth distribution functions: how often the rate changes • Every 3 seconds • Every 200ms • Fit the scale values(1 through 5) on a normal curve [Floyd ‘00]

  27. Future Work • Accurately determine the threshold below which temporal scaling is unacceptable • More accurate bandwidth distribution function • Hybrid scaling methods (Quality + Temporal) • Audio Scaling

  28. Conclusions • Application level solution to the problem of congestion due to unresponsive video streams • Developed a mechanism to quantify the amount of change in a video stream • Shown that content aware scaling can improve user perceived quality by as much as 50% • Developed a system to do adaptive content-aware scaling and are in the process of determining it impact on user perceived quality

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