Most providers of video streaming are interested in improving quality of user experience across various wireless network circumstances. Many buffer-based ABR algorithms have been proposed to adjust the bitrate accordi...
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Most providers of video streaming are interested in improving quality of user experience across various wireless network circumstances. Many buffer-based ABR algorithms have been proposed to adjust the bitrate according to the buffer occupancy at the client player. Though keeping the buffer occupancy stable, such ABR algorithms cannot provide satisfied responsiveness to bandwidth dynamics. To solve the problem, we propose an ABR algorithm BBA+ that iteratively corrects the mapping function between bitrate and buffer occupancy according to network throughput. The results show that BBA+ effectively improves the average bitrate and reduces the rebuffering ratio.
adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To the purpose, video players host a controller that dynamically chooses the most suitable video represe...
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adaptive video streaming systems are expected to provide the best user experience to improve service engagement. To the purpose, video players host a controller that dynamically chooses the most suitable video representation to be downloaded. It is well-known that finding one tuning of the controller's parameters which performs satisfactorily in a wide range of scenarios is very challenging. This paper studies the problem of providing users with (near) optimal Quality of Experience (QoE) for Dynamic adaptive Streaming over HTTP (DASH) systems. We present ERUDITE, a closed-loop system to optimally tune - at run-time - the adaptive streaming controller's parameters to adapt to changing scenario's parameters. ERUDITE employs a Deep Neural Network (DNN) which continuously provides the streaming controller with estimates of optimal parameters based on measured metrics such as bandwidth samples and overall obtained QoE. The DNN is trained using a dataset that we have built by finding, for thousands of realistic scenarios, robust optimal adaptive streaming controller's parameters using a Bayesian optimization algorithm. Results, gathered considering a large number of diverse scenarios, show that ERUDITE is able to provide near optimal performances by reducing impairments due to rebuffering and video level switching.
On-demand streaming video traffic is managed by an adaptivebitrate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network...
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ISBN:
(纸本)9798400702365
On-demand streaming video traffic is managed by an adaptivebitrate (ABR) algorithm whose job is to optimize quality of experience (QoE) for a single video session. ABR algorithms leave the question of sharing network resources up to transport-layer algorithms. We observe that as the internet gets faster relative to video streaming rates, this delegation of responsibility gives video traffic a burstier on-off traffic pattern. In this paper, we show we can substantially smooth video traffic to improve its interactions with the rest of the internet, while maintaining the same or better QoE for streaming video. We smooth video traffic with two design principles: application-informed pacing, which allows ABR algorithms to set an upper limit on packet-by-packet throughput, and by designing ABR algorithms that work with pacing. We propose a joint ABR and rate-control scheme, called Sammy, which selects both video quality and pacing rates. We implement our scheme and evaluate it at a large video streaming service. Our approach smooths video, making it a more friendly neighbor to other internet applications. One surprising result is that being friendlier requires no compromise for the video traffic: in large scale, production experiments, Sammy improves video QoE over an existing, extensively tested and tuned production ABR algorithm.
adaptivebitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the f...
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adaptivebitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, 3G HSDPA data set from the Internet, as well as the 4G/LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in *** at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.
The performance of adaptivebitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated...
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The application of deep reinforcement learning (DRL) to computer and networked systems has recently gained significant popularity. However, the obscurity of decisions by DRL policies renders it hard to ascertain that ...
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ISBN:
(纸本)9781450383837
The application of deep reinforcement learning (DRL) to computer and networked systems has recently gained significant popularity. However, the obscurity of decisions by DRL policies renders it hard to ascertain that learning-augmented systems are safe to deploy, posing a significant obstacle to their real-world adoption. We observe that specific characteristics of recent applications of DRL to systems contexts give rise to an exciting opportunity: applying formal verification to establish that a given system provably satisfies designer/user-specified requirements, or to expose concrete counter-examples. We present whiRL, a platform for verifying DRL policies for systems, which combines recent advances in the verification of deep neural networks with scalable model checking techniques. To exemplify its usefulness, we employ whiRL to verify natural requirements from recently introduced learning-augmented systems for three real-world environments: Internet congestion control, adaptive video streaming, and job scheduling in compute clusters. Our evaluation shows that whiRL is capable of guaranteeing that natural requirements from these systems are satisfied, and of exposing specific scenarios in which other basic requirements are not.
Full Paper in the Innovative Practice track - The pandemics caused by the spreading of the COVID 19 virus cornered the educational system worldwide, changing the classroom into remote class activities. This change in ...
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ISBN:
(纸本)9781665438513
Full Paper in the Innovative Practice track - The pandemics caused by the spreading of the COVID 19 virus cornered the educational system worldwide, changing the classroom into remote class activities. This change in our social behavior has directly impacted the volume and shape of the Internet traffic data. A recent study shows 15% to 30% increases in Internet traffic caused, among other reasons, by educational video streaming traffic during few weeks in the 2020 lockdown period in Europe. To give some perspective, network providers usually work with a 30% data traffic increase per year. In 2021, it is expected that almost 82% of all Internet traffic will be video, according to CISCO annual forecast report. This scenario has a tremendous impact on the Internet bandwidth capacity, demanding optimized video streaming solutions, such as adaptive bitrate algorithms (ABR). On the other hand, considering the educational challenges in computer network courses, the core activities must be executed using specialized infrastructure to develop students' capabilities with networking equipment. As these types of equipment are costly to be obtained and forwarded to in-home students or simply e-students, a remote platform capable of reproducing an environment for networking applications is required. This is the scenario where PyDash was built. PyDash is a framework for the development of adaptive streaming video algorithms. It is a learning tool designed to abstract the networking communication details, allowing e-students to focus exclusively on developing and evaluating ABR protocols. This paper presents our practical experience developing and using PyDash as an educational tool for teaching ABR protocols at Computing Networking courses at the Department of Computer Science at the University of Brasilia, Brazil. Last semester, over 120 students, divided into four different undergraduate courses, had their first contact with PyDash. Even though this was their first contact with
The performance of adaptivebitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated...
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The performance of adaptivebitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput;and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters;and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.
adaptivebitrate Algorithm (ABR) is a category of important technique to improve the Quality of Experience (QoE) of video streaming service. Many ABR algorithms have been proposed recently and achieved good results. H...
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ISBN:
(纸本)9781538695524
adaptivebitrate Algorithm (ABR) is a category of important technique to improve the Quality of Experience (QoE) of video streaming service. Many ABR algorithms have been proposed recently and achieved good results. However, there are still some problems to be solved: (1) These ABR algorithms are based on open source data and evaluated by only one simulated player;(2) The QoE metrics are defined differently and their parameters are set by experience with little explanation;(3) These algorithms consider only the QoE of users but ignore the bandwidth cost of video service providers. In order to solve these problems, we propose a Real-time Evaluation System for ABR (RESA) which do evaluations by real online users. A reasonable QoE metric is also introduced by setting its parameters based on user preferences during video watching. We evaluate the state-of-the-art ABR algorithms on RESA with real video streaming service. Finally, we further introduce bitrate controlling into the adaptivebitrate Algorithm (ABRbc) to solve the bandwidth cost problem for online ABR.
Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new...
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ISBN:
(纸本)9781450359566
Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new class of applications from traditional live streaming is that these live streams are watched by viewers at different delays;while some viewers watch a live stream in real-time, others view the content in a time-shifted manner at different delays. In the presence of variability in the upload bandwidth, which is typical in mobile environments, existing solutions silo viewers into either receiving low latency video at a lower quality or a higher quality video with a significant delay penalty, without accounting for the presence of diverse time-shifted viewers. In this paper, we present Vantage, a live-streaming upload solution that improves the overall quality of experience for diverse time-shifted viewers by using selective quality-enhancing retransmissions in addition to real-time frames, optimizing the encoding schedules to balance the allocation of the available bandwidth between the two. Our evaluation using real-world mobile network traces shows that Vantage can provide high quality simultaneously for both low-latency and delayed viewing. For delayed viewing, Vantage achieves an average improvement of 19.9% over real-time optimized video streaming techniques across all the network traces and test videos, with observed gains of up to 42.9%. These benefits come at the cost of an average drop in real-time quality of 3.3%, with a maximum drop of 7.1%. This represents a significant performance improvement over current techniques used for SLVS applications, which primarily optimize the video upload for real-time viewing.
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