Optimal Set of Video Representations in Adaptive Streaming

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Paper presented at MMSys'14. Available here:
http://dl.acm.org/citation.cfm?id=2557652&CFID=426109007&CFTOKEN=22501685

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several streams for each video. Each stream has a different resolution and bit rate, aimed at a specific set of users, e.g., TV, mobile phone. While most existing works on adaptive streaming deal with optimal playout-control strategies at the client side, in this paper we concentrate on the providers’ side, showing how to improve user satisfaction by optimizing the encoding parameters. We formulate an integer linear program that maximizes users’ average satisfaction, taking into account the network characteristics, the type of video content, and the user population. The solution of the optimization is a set of encoding parameters that outperforms commonly used vendor recommendations, in terms of user satisfaction and total delivery cost. Results show that video content information as well as network constraints and users’ statistics play a crucial role in selecting proper encoding parameters to provide fairness among users and reduce network usage. By combining patterns common to several representative cases, we propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content.

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Optimal Set of Video Representations in Adaptive Streaming

  1. 1. Optimal Set of Video Representations in Adaptive Streaming L. Toni1 , R. Aparicio2 , G. Simon2 , A. Blanc2 and P. Frossard1 1: EPFL 2: Telecom Bretagne
  2. 2. Context : Video Delivery Chain Content provider ingest server CDN origin server edge servers end-users 2 / 17 Gwendal Simon Optimal Video Representations in DASH
  3. 3. Context : Video Delivery Chain Content provider ingest server CDN origin server edge servers end-users Adaptive Streaming 1 video stream = k representations 2 / 17 Gwendal Simon Optimal Video Representations in DASH
  4. 4. Context : Video Delivery Chain Content provider ingest server CDN origin server edge servers end-users many works on adaptive video delivery 2 / 17 Gwendal Simon Optimal Video Representations in DASH
  5. 5. Context : Video Delivery Chain Content provider ingest server CDN origin server edge servers end-users many works on client adaptation 2 / 17 Gwendal Simon Optimal Video Representations in DASH
  6. 6. Context : Video Delivery Chain Content provider ingest server CDN origin server edge servers end-users what about video encoding for adaptive streaming ? 2 / 17 Gwendal Simon Optimal Video Representations in DASH
  7. 7. Encoding a set of representations For a content provider, what does it mean ? 3 / 17 Gwendal Simon Optimal Video Representations in DASH
  8. 8. Encoding a set of representations For each video in the catalog, deciding : How many representations What resolutions What bit-rates 3 / 17 Gwendal Simon Optimal Video Representations in DASH
  9. 9. Encoding a set of representations For each video in the catalog, deciding : How many representations What resolutions What bit-rates Today : Recommendations from vendors (e.g. Apple) Self-tuned by content providers (e.g. Netflix) 3 / 17 Gwendal Simon Optimal Video Representations in DASH
  10. 10. Our objective Finding the best set of representations for each video in a catalog 4 / 17 Gwendal Simon Optimal Video Representations in DASH
  11. 11. Our objective Finding the best set of representations for each video in a catalog maximizing QoE of clients 4 / 17 Gwendal Simon Optimal Video Representations in DASH
  12. 12. Our objective Finding the best set of representations for each video in a catalog maximizing QoE of clients with limited infrastructure cost 4 / 17 Gwendal Simon Optimal Video Representations in DASH
  13. 13. Our contributions 1. We formulate an optimization problem 5 / 17 Gwendal Simon Optimal Video Representations in DASH
  14. 14. Our contributions 1. We formulate an optimization problem 2. We study how optimal are recommended sets 5 / 17 Gwendal Simon Optimal Video Representations in DASH
  15. 15. Our contributions 1. We formulate an optimization problem 2. We study how optimal are recommended sets 3. We identify guidelines for encoding parameters 5 / 17 Gwendal Simon Optimal Video Representations in DASH
  16. 16. Model 6 / 17 Gwendal Simon Optimal Video Representations in DASH
  17. 17. Definitions (v, r, s) : representation of video v encoded at resolution s and at bit-rate r each user is associated with one video resolution fvs(r) ∈ [0, 1] : user satisfaction
  18. 18. Definitions (v, r, s) : representation of video v encoded at resolution s and at bit-rate r each user is associated with one video resolution fvs(r) ∈ [0, 1] : user satisfaction normalized QoE per resolution
  19. 19. Definitions (v, r, s) : representation of video v encoded at resolution s and at bit-rate r each user is associated with one video resolution fvs(r) ∈ [0, 1] : user satisfaction 0 2,000 4,000 6,000 8,000 0.6 0.7 0.8 0.9 1 rate (in kbps) (1-VQM)normalized 224p 360p 720p 1080p 7 / 17 Gwendal Simon Optimal Video Representations in DASH
  20. 20. Constraints The global CDN capacity C The total number of representations K The fraction of users that must be served P 8 / 17 Gwendal Simon Optimal Video Representations in DASH
  21. 21. ILP max {ααα,βββ} u∈U v∈V r∈R s∈S fvrs · αuvrs (1a) s.t. αuvrs ≤ βvrs , u ∈ U, v ∈ V, r ∈ R, s ∈ S (1b) βvrs ≤ u∈U αuvrs , v ∈ V, r ∈ R, s ∈ S (1c) (b min vs − br ) · βvrs ≤ 0, v ∈ V, r ∈ R, s ∈ S (1d) (br − b max vs ) · βvrs ≤ 0, v ∈ V, r ∈ R, s ∈ S (1e) r∈R αuvrs ≤ 1, if v = vu & s = su 0, otherwise u ∈ U, v ∈ V, s ∈ S (1f) v∈V r∈R s∈S br · αuvrs ≤ cu, u ∈ U (1g) u∈U v∈V r∈R s∈S br · αuvrs ≤ C, (1h) ... (1i) 9 / 17 Gwendal Simon Optimal Video Representations in DASH
  22. 22. Recommended set performance 10 / 17 Gwendal Simon Optimal Video Representations in DASH
  23. 23. Process 1. We define some configurations : A model for QoE based on four test sequences A population of users Settings for the content provider 11 / 17 Gwendal Simon Optimal Video Representations in DASH
  24. 24. Process 1. We define some configurations : A model for QoE based on four test sequences A population of users Settings for the content provider 2. We use CPLEX to compute the optimal representations and obtain the best QoE possible 11 / 17 Gwendal Simon Optimal Video Representations in DASH
  25. 25. Process 1. We define some configurations : A model for QoE based on four test sequences A population of users Settings for the content provider 2. We use CPLEX to compute the optimal representations and obtain the best QoE possible 3. We compare with the best QoE achievable with the recommended sets 11 / 17 Gwendal Simon Optimal Video Representations in DASH
  26. 26. How far from the optimal ? For a catalog of four videos in a regular configuration 20 30 40 50 60 0.8 0.85 0.9 0.95 1 Apple (40 rep) number of representations K avg.usersatisfaction 12 / 17 Gwendal Simon Optimal Video Representations in DASH
  27. 27. How far from the optimal ? For a catalog of four videos in a regular configuration 20 30 40 50 60 0.8 0.85 0.9 0.95 1 Apple (40 rep) number of representations K avg.usersatisfaction 12 / 17 Gwendal Simon Optimal Video Representations in DASH
  28. 28. How far from the optimal ? For a catalog of four videos in a regular configuration 20 30 40 50 60 0.8 0.85 0.9 0.95 1 Apple (40 rep) 19 rep. number of representations K avg.usersatisfaction 12 / 17 Gwendal Simon Optimal Video Representations in DASH
  29. 29. Guidelines 13 / 17 Gwendal Simon Optimal Video Representations in DASH
  30. 30. Process 1. We define multiple configurations with variation of The popularity of videos The characteristics of clients The constraints of the service provider 14 / 17 Gwendal Simon Optimal Video Representations in DASH
  31. 31. Process 1. We define multiple configurations with variation of The popularity of videos The characteristics of clients The constraints of the service provider 2. We identify trends and derive useful guidelines for the settings of encoding parameters 14 / 17 Gwendal Simon Optimal Video Representations in DASH
  32. 32. How many representations per video ? 224p 360p 720p 1080p 0 2 4 6 Resolution numberofrepresentations cartoon documentary sport movie 15 / 17 Gwendal Simon Optimal Video Representations in DASH
  33. 33. How many representations per video ? 224p 360p 720p 1080p 0 2 4 6 Resolution numberofrepresentations cartoon documentary sport movie nb. of representations depends on the content 1 15 / 17 Gwendal Simon Optimal Video Representations in DASH
  34. 34. How many representations per video ? 224p 360p 720p 1080p 0 2 4 6 Resolution numberofrepresentations cartoon documentary sport movie slightly more representations for high resolutions 2 15 / 17 Gwendal Simon Optimal Video Representations in DASH
  35. 35. Conclusion 16 / 17 Gwendal Simon Optimal Video Representations in DASH
  36. 36. Conclusion For more details, read the paper ! 17 / 17 Gwendal Simon Optimal Video Representations in DASH
  37. 37. Conclusion For more details, read the paper ! Takeaway : Optimal encoding parameters for adaptive streaming Recommendations far from being optimal Some useful guidelines 17 / 17 Gwendal Simon Optimal Video Representations in DASH
  38. 38. Conclusion For more details, read the paper ! Takeaway : Optimal encoding parameters for adaptive streaming Recommendations far from being optimal Some useful guidelines Future works : How to take into account dynamic configurations ? How to optimize encoding in a data-center ? 17 / 17 Gwendal Simon Optimal Video Representations in DASH

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