1. Video through a Crystal Ball:
Effect of Bandwidth Prediction Quality on Adaptive Streaming in Mobile Environments
Tarun ManglaT, Nawanol Theera-Amprnount*, Mostafa AmmarT,
Ellen ZeguraT, Saurabh Bagchi* * T

2. Outline Motivation System Context Adaptation Algorithm Evaluation
Conclusion

3. Outline Motivation System Context Adaptation Algorithm Evaluation
Conclusion

4. Video dominates Mobile Internet
Mobile video traffic accounted for 55 percent of total mobile data traffic in 2015
Video streaming: Why is it important? What technology is currently used? Explain ABR mechanism in detal
Give source for this figure
Adaptive bitrate (ABR) streaming is used to handle diversity of clients
Source: Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2015–2020 White Paper

5. Overview of ABR streaming?
Network
Explain DASH streaming
720p
480p
Video is divided into chunks
Each chunk is encoded at multiple bitrates
360p

6. Overview of ABR streaming?
Network
Explain DASH streaming
Adaptation Algorithm
HTTP GET “Yellow”
Adaptation algorithm located at client decides the quality of chunks to download

7. Overview of ABR streaming?
Network
Explain DASH streaming
Adaptation Algorithm
HTTP GET “Red”

8. Overview of ABR streaming?
Network
Explain DASH streaming
Adaptation Algorithm
HTTP GET “Red”

9. Overview of ABR streaming?
Network
Explain DASH streaming
Different implementations of adaptation algorithm with different adaptation logic
Adaptation Algorithm
HTTP GET “Green”

10. Adaptation algorithm goal
Bitrate adaptation algorithm aims to improve the Quality of Experience (QoE) of streaming.
QoE is characterized by:
Number of bitrate switches
Rebuffering ratio
Average bitrate

11. Existing adaptation approaches
Rate-based
Adapt based on past chunk throughput
Buffer-based
Adapt based on current buffer occupancy
Most of the video players use a hybrid of these two approaches.

12. Existing adaptation approaches
Existing approaches use information from past (or present) making them reactive
They have to experience the bandwidth fluctuation in order to infer it
time
Bandwidth
Bandwidth decreased! adapt!

13. Stationary clients vs Mobile clients
Mobile client: Low bandwidth, high fluctuation and intermittent connectivity. Substantiate using figures.
Stationary client: Connected to home WiFi
Mobile client: Connected to campus WiFi while on a bus
Mobile clients usually have:
Lower average bandwidth
High bandwidth fluctuations
Intermittent connectivity
Reactive adaptation
Degradation in QoE

14. Prediction-based adaptation
Network
Adaptation Algorithm
Proactive end-to-end available bandwidth predictions
Bandwidth prediction
Decouple adaptation and bandwidth prediction
Adapt using proactive bandwidth predictions

15. Bandwidth prediction system
Our focus
How does prediction quality, namely horizon and accuracy impact the performance of prediction-aware adaptation system?
Under what bandwidth profiles is it most useful to have predictions?
How does prediction-based streaming architecture interact with video system parameters?
Network
Adaptation Algorithm
Bandwidth prediction system
Focus on defining the characteristics this system should have in order to be useful. When is it most critical to have such system. Interaction of such system with video system parameter. Not focusing on how to design such bandwidth prediction system, but on
Bandwidth prediction

16. Outline Background and Motivation System Context
CrystalBall Adaptation Algorithm
Evaluation
Discussion

17. System context Prediction Model Bandwidth prediction vector , where
Quantify error in prediction ( ) as,
Video Model
Video of M chunks of length L, each encoded in bitrate set
is the current buffer and is the maximum buffer

18. Outline Background and Motivation System Context Adaptation Algorithm
Evaluation
Discussion

19. CrystalBall Adaptation
No errors in prediction
Errors in prediction
Clear CrystalBall Adaptation (CCB)
Foggy CrystalBall Adaptation (FCB)

20. Clear CrystalBall Algorithm (CCB)
Input
: current buffer
: b/w prediction
Video parameters
Adaptation algorithm
: target bitrate
An MILP optimization problem can be formulated with these constraints, but solving such problem is time consuming
Maximize :
Constraint : No rebuffering
max-min has a side effect of reducing bitrate switches

21. Clear CrystalBall Algorithm (CCB)
Intuition: bandwidth at time t can be used to download chunk with playback time greater than t
Bandwidth
Time
Bandwidth
Slots 1 2 3 4
Divide time into n slots based on chunk playback time

22. Clear CrystalBall Algorithm (CCB)
Bandwidth
Slots 1 2 3 4
Bandwidth
Slots 1 2 3
If bandwidth of current slot greater than next, then merge the two slots

23. Clear CrystalBall Algorithm (CCB)
Bandwidth
Slots 1 2 3
Bandwidth
Slots 1 2
Iterate till no more merging is possible
Assign bitrate to each slot

24. Foggy CrystalBall Algorithm (FCB)
What if there are errors in predictions?
Overestimation ( ) : switch-up and rebuffering
Underestimation ( ) : switch-down and underutilization
FCB = CCB + Error Mitigation Heuristic
Error mitigation heuristic tries to avoid (wrong) switches

25. Foggy CrystalBall Algorithm (FCB)
FCB = CCB + Error Mitigation Heuristic
Error mitigation heuristic
A switch-up decision could be because of over-estimation. Switch-up only if bandwidth is greater than 1+alpha times target bitrate. Here alpha is a safety parameter and is a function of how much over estimation
A switch-down decision could be because of under-estimation. We could avoid this switch-down but if the buffer is too low, we should switch-down
: target bitrate
: predicted bw
Switch up :
Switch down :
: current buffer
: maximum buffer

26. Outline Background and Motivation System Context Adaptation Algorithm
Evaluation
Discussion

27. Evaluation When is it most useful to have predictions?
How does prediction quality, namely horizon and accuracy impact the performance of prediction-aware adaptation system?
How does prediction-based streaming architecture interact with video system parameters?
Focus on defining the characteristics this system should have in order to be useful. When is it most critical to have such system. Interaction of such system with video system parameter. Not focusing on how to design such bandwidth prediction system, but on

28. Experimental Setup Bandwidth traces Synthetic traces
Video parameters and metrics:
10 minute long video, chunk size 4s
6 bitrate levels (in kbps): {150, 300, 600, 1200, 2000 ,3000}
Maximum buffer size : 32s
QoE: Rebuffering ratio, Average bitrate, Number of bitrate switches
Bandwidth traces
Synthetic traces
Campus WiFi traces
Observed bandwidth while riding a bus
40 traces, each 15 min long

29. Experimental Setup RBA : Harmonic Mean of last 5 chunks
Video Client: A simulator written in Python
Input : bandwidth traces and video parameters
Four adaptation algorithms
Rate-based adaptation (RBA), [CoNext, 2012]
Buffer-based adaptation (BBA), [Sigcomm, 2014 ]
Prediction-based adaptation (PBA), [HotMobile, 2015]
CrystallBall adaptation (CCB, FCB)
Player takes into i
RBA : Harmonic Mean of last 5 chunks
BBA : Current buffer occupancy
PBA : Average of predicted bandwidth

30. When are predictions most useful?
Used a rectangular waveform to model fluctuating bandwidth profile

31. Amplitude of fluctuations
L = 30s, D = 0.5
Predictions are useful under high amplitude fluctuations

32. Performance on campus-wifi traces
RBA and PBA have high rebuffering
BBA has high switches
CCB performs well across all metrics

33. How does prediction quality impacts performance?
Prediction window
Errors in prediction

34. Effect of Prediction Window
QoE increases with increase in prediction window
Benefits of prediction diminish (80s here) after a horizon

35. Effect of errors Prediction errors depend on prediction system
Intuitively, probability of error increases as we predict farther into future.
Consider time-varying errors,

36. Effect of errors FCB can mitigate the errors in prediction
PBA w/ errors has higher rebuffering
alpha = 0.2, beta = 0.6 : FCB can alleviate errors with proper error mitigation heuristic
PBA w/ errors has higher switches
FCB can mitigate the errors in prediction

37. Role of video system parameters?
Buffer size
Bitrate granularity

38. Prediction-aware adaptation can take advantage of buffer size
Effect of buffer size
CCB bitrate increases with increase in maximum buffer size. CCB, number of bitrate switches decrease but remain same in RBA
Prediction-aware adaptation can take advantage of buffer size

39. Conclusion Designed a prediction-aware adaptation algorithm
Characterized bandwidth profiles when predictions are useful
Using Crystallball adaptation, explored the effects of errors and prediction window on performance of adaptive streaming
Examined the role of video system parameters in prediction-aware streaming

40. Questions?

41. Backup Slides

42. Bandwidth Prediction System
Cross-layer approach [CQIC, HotMobile 2015]
Location-based [BreadCrumbs, MobiCom 2008]
Real-time data at network [TANGO, HotDep 2013]
Shaping at network [IFIP Networking, 2015]

43. Buffer-based adaptation

44. PBA vs CrystalBall Short term vs long term predictions
Optimization function
Adaptation heuristic
Do no consider errors in prediction

45. Frequency of fluctuations

46. Duration of intermittent connectivity

47. Trade-off between window and error
Larger window size: more errors
Small window size: less information
Large prediction window, more information but more errors
Small prediction window, less errors but less information

48. Bitrate granularity
Prediction-based adaptation can cope with increasing bitrate granularity.