Optimal Adaptation Trajectories for Block-Request Adaptive Video Streaming
Summary (3 min read)
I. INTRODUCTION
- Recently, video streaming has become one of the biggest sources of traffic on the Internet [1] .
- Even in an indoor residential or office WLAN, the static user is typically exposed to interference, cross-traffic, and fading effects.
- It also offers reliable communication by means of retransmitting lost packets, which enables usage of efficient video compression technologies that are particularly sensitive to packet losses (the loss of an I-frame may result in several seconds of corrupted playback).
- Neither is any of the existing clients widely accepted as a basis for performance comparison.
III. OPTIMAL CLIENT
- The authors present the optimization metric that they use, and their approach to calculating optimal adaptation trajectories.
- Main factors that influence QoE include (i) duration and distribution of frozen frames, (ii) properties of the adaptation trajectory, such as minimum bit-rate, average bit-rate, frequency and magnitude of bit-rate switches, and (iii) start-up delay.
- Therefore, the authors use the following objectives and constraints for optimization of adaptation trajectories.
- Obviously, the earliest time when the playback can start is when the MPD file and the first segment of the representation with the lowest bit-rate are downloaded.
- Therefore, the authors subsequently solve the following optimization problem OP2 in oder to select the optimum solution of OP1 that has the minimum number of quality switches.
A. DASH client
- The details on the adaptation logic of their DASH client are provided in [18] .
- A prototype has been implemented as a plugin for the VLC player [19] .
- If, however, the throughput is high enough, no delays are introduced, the buffer level rises above β max , and the video quality is decreased until the buffer level starts to decrease again.
- It takes into account the current throughput, its fluctuations, as well as the buffer level, which can be interpreted as the integrated mismatch between the throughput and the video bit-rate.
- They control its sensitivity to bandwidth fluctuations, speed of convergence, efficiency of bandwidth utilization, and the probability of buffer underruns.
B. Microsoft SmoothStreaming client
- The MSS streaming solution is supported by most major Internet browsers and is deployed by several popular video on demand websites.
- The MSS client was used in several studies on BRAS client behavior and was found to outperform several competing approaches.
- Since the implementation of MSS is closed, the exact operation of its adaptation logic is not known.
- The MSS client version the authors used (Silverlight 5.1.20125.0) seems to maintain a target buffer level of approximately 30 seconds.
- In the buffering phase, the player will request the segments as fast as possible to fill the buffer.
A. Setting
- The emulated part of The video traffic was always routed via the station with the second lowest maximum throughput of 1.7 Mbps.
- In addition to the video traffic, the authors generated synthetic background traffic mimicking the behavior of 14 HTTP clients, based on the stochastic model from Pries at al. [22] .
- The authors assured that the differences in segment sizes between DASH and MSS was small enough to be negligible.
- Depending on the format of the manifest file, the client might not know the actual segment bit-rate (and thus Fig. 2 .
- Mean, minimum, maximum (the latter two are shown as percentage of the mean), also known as Video bit-rate variation.
B. Experiment 1
- In this experiment the authors evaluate two metrics.
- The top figure depicts the increase of the average video bit-rate of optimal trajectories depending on the start-up delay, for two numbers of available video representations: 6 and 14.
- Maybe somewhat surprisingly, the improvement from a start-up delay of 60 sec is only around 12%.
- Note that in real deployment scenarios, a high number of available video representations might be quite unrealistic, since it results in higher encoding and storage costs for content providers.
- Thus, the higher amount of quality switches for 14 representations might partially be explained by the inexact calculations.
C. Experiment 2
- The authors compare the performance of the MSS client, and of the DASH client with different configurations, with optimal performance.
- In all configurations of the DASH client, the critical buffer threshold was set to β crit = 2s.
- The lower threshold β min of the target buffer interval varied between 5s and 20s.
- The authors observe that the DASH client always outperforms the MSS client w.r.t. the average video bit-rate (78% to 90% of the optimum vs. 62%).
- It also outperforms the MSS client w.r.t. the total duration of frozen frames, and, for some of the configurations, w.r.t. the number of quality switches.
D. Experiment 3
- The traffic of the two clients were routed over the same wireless station.
- Figure 4b shows various performance metrics as Figure 4a did for the case of a single client, except that the values are now averages over the runs and over the two clients.
- In fact, the authors observe that the conclusions from experiment 2 still hold for the two clients setting.
- The authors observe that optimal trajectories have almost perfect fairness since all values are approximately 0.
- Further, the authors observe that the fairness of their DASH client is comparable or better than that of the MSS client.
VI. CONCLUSION
- Further, the authors evaluated the behavior of the widely deployed MSS video streaming client and their own client supporting the recently adopted DASH standard in a WLAN, and compared their performance with the optimum.
- It also includes performance evaluation under a broader spectrum of network conditions.
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...A two-step approach for modeling the optimal QoE adaptation for a single user is provided in [30]....
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References
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...[7] experimentally evaluated the MSS, ADS, and the Netflix HAS clients....
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...[9] also analyze the impact, bandwidth estimation techniques have on the behavior of streaming clients....
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Frequently Asked Questions (19)
Q2. What are the future works mentioned in the paper "Optimal adaptation trajectories for block-request adaptive video streaming" ?
Future work includes a study of adaptation strategies that, based on short-term prediction of TCP throughput and subsequent solutions of presented optimization problems OP1 and OP2 over small and medium time horizons, might increase the client ’ s performance towards the achievable optimum.
Q3. What is the main challenge in designing efficient adaptation strategies?
The main challenge in designing efficient adaptation strategies is that path throughput is a random process, whose value is difficult to reliably predict for the relevant time horizon, ranging from several seconds to several tens of seconds.
Q4. What was the use of the throughput process of the TCP flow?
The throughput process of the TCP flow was then used as input V (t) for optimization problems OP1 and OP2, to calculate optimal adaptation trajectories.
Q5. What are the main issues that have moved into the research community?
performance and fairness issues with multiple streaming clients competing for bottleneck bandwidth moved into the focus of the research community.
Q6. How does MSS perform in a WLAN?
It turned out that their client is able to outperform the MSS client in the evaluated setting by achieving an average video bit-rate that is between 78% and 90% of the optimum, while MSS achieves approximately 62%, with less quality fluctuations, less time spent in re-buffering, comparable fairness when two clients share a bottleneck link, and a lower average buffer level, which is especially beneficial for live content.
Q7. What is the effect of the low values on the video quality?
Low values let the video client switch the video quality more often in order to more closely follow throughput fluctuations and thus increase the average video quality by better utilizing the available bandwidth.
Q8. How long did the emulation take to complete?
The one-way delays on the emulated links connecting the stations and the access points with Ethernet interfaces of the emulation host were set to a constant value of 1 ms.
Q9. What is the important metric for the analysis of streaming clients?
Jiang et al. [8] argue that the throughput estimation component is crucial for the behavior of a streaming client and compares 4 estimators, of which harmonic mean turns out to perform best.
Q10. What is the value of the buffer level?
In order to avoid buffer underruns by all means, if the buffer level falls below a configurable critical threshold βcrit, the lowest video quality is selected immediately.
Q11. What are the parameters that can be tuned to optimize the behavior of the MSS client?
They control its sensitivity to bandwidth fluctuations, speed of convergence, efficiency of bandwidth utilization, and the probability of buffer underruns.
Q12. Why did the authors use the throughput process as recorded by the video client?
The authors didn’t use the throughput process as recorded by the video client since it may be suboptimal, because video clients typically introduce gaps between subsequent requests in order to prevent their buffer level from exceeding a certain threshold.
Q13. What is the effect of the buffer interval on the video quality?
low values increase the risk of buffer underruns but they increase the ”liveness” of the streaming session, which is important for transmission of live content.
Q14. How many Mbps is the average throughput of the emulation server?
To give an impression of the link qualities, a TCP transfer of 100MB of data between the access point and each individual station achieves an average throughput of approximately (in [Mbps]): 1.4, 1.7, 19, 19, 21, 21, 21.
Q15. How does it switch between the representations?
It then switches between the representations step by step in a smooth manner in order to avoid abrupt changes of quality that might impact QoE.
Q16. What were the metrics used to evaluate the streaming client?
The metrics were: (i) average bit-rate during the streaming session, (i) number of quality switches, and (iii) time spent in re-buffering.
Q17. What is the average number of switches required by the optimal trajectory?
Also note that the average number of switches required by the optimal trajectory is as low as approximately 2 (and, in fact, might be even lower due to the tolerance gap allowed in the optimization process).
Q18. What is the author's approach to calculating optimal adaptation trajectories?
The authors perform a numerical evaluation of the approach using fixed, uniform and normal distributions of the available bandwidth.
Q19. Why is the DASH client able to use all the available bandwidth?
The reason is that despite the high fluctuation of the wireless link throughput (fading plus cross-traffic), 6 representations are enough to utilize all the available bandwidth, as the authors will also see in experiment 2.