163 research outputs found
Karma: Adaptive Video Streaming via Causal Sequence Modeling
Optimal adaptive bitrate (ABR) decision depends on a comprehensive
characterization of state transitions that involve interrelated modalities over
time including environmental observations, returns, and actions. However,
state-of-the-art learning-based ABR algorithms solely rely on past observations
to decide the next action. This paradigm tends to cause a chain of deviations
from optimal action when encountering unfamiliar observations, which
consequently undermines the model generalization. This paper presents Karma, an
ABR algorithm that utilizes causal sequence modeling to improve generalization
by comprehending the interrelated causality among past observations, returns,
and actions and timely refining action when deviation occurs. Unlike direct
observation-to-action mapping, Karma recurrently maintains a multi-dimensional
time series of observations, returns, and actions as input and employs causal
sequence modeling via a decision transformer to determine the next action. In
the input sequence, Karma uses the maximum cumulative future quality of
experience (QoE) (a.k.a, QoE-to-go) as an extended return signal, which is
periodically estimated based on current network conditions and playback status.
We evaluate Karma through trace-driven simulations and real-world field tests,
demonstrating superior performance compared to existing state-of-the-art ABR
algorithms, with an average QoE improvement ranging from 10.8% to 18.7% across
diverse network conditions. Furthermore, Karma exhibits strong generalization
capabilities, showing leading performance under unseen networks in both
simulations and real-world tests
Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning
Learning-based Adaptive Bit Rate~(ABR) method, aiming to learn outstanding
strategies without any presumptions, has become one of the research hotspots
for adaptive streaming. However, it typically suffers from several issues,
i.e., low sample efficiency and lack of awareness of the video quality
information. In this paper, we propose Comyco, a video quality-aware ABR
approach that enormously improves the learning-based methods by tackling the
above issues. Comyco trains the policy via imitating expert trajectories given
by the instant solver, which can not only avoid redundant exploration but also
make better use of the collected samples. Meanwhile, Comyco attempts to pick
the chunk with higher perceptual video qualities rather than video bitrates. To
achieve this, we construct Comyco's neural network architecture, video datasets
and QoE metrics with video quality features. Using trace-driven and real-world
experiments, we demonstrate significant improvements of Comyco's sample
efficiency in comparison to prior work, with 1700x improvements in terms of the
number of samples required and 16x improvements on training time required.
Moreover, results illustrate that Comyco outperforms previously proposed
methods, with the improvements on average QoE of 7.5% - 16.79%. Especially,
Comyco also surpasses state-of-the-art approach Pensieve by 7.37% on average
video quality under the same rebuffering time.Comment: ACM Multimedia 201
Improving The Efficiency Of Video Transmission In Computer Networks
In-depth examination of current techniques for enhancing the efficiency of video transmission over digital networks is provided in this study. Due to the growing need for high-quality video content, optimizing video transmission is an important area of research. This review categorizes and in-depth examines a range of methods proposed in the literature to enhance video transmission effectiveness. ABR, DNN architecture, adaptive streaming, Quality of Service (QoS), error resilience, congestion control, video compression, and hardware acceleration for video provisioning are just a few of the cutting-edge techniques that are covered in the discussion, which ranges from the more traditional to the cutting-edge. This essay provides a methodical evaluation of the numerous tactics that are available, along with an analysis of their guiding principles, advantages, and disadvantages. The paper also offers a comparative analysis of various approaches, highlighting trends, gaps, and potential future research directions in this crucial domain, all of which help to create more efficient video compression and transmission paradigms in computer networks
Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services
Adaptive bitrate (ABR) algorithms are used to adapt the video bitrate based
on the network conditions to improve the overall video quality of experience
(QoE). Recently, reinforcement learning (RL) and asynchronous advantage
actor-critic (A3C) methods have been used to generate adaptive bit rate
algorithms and they have been shown to improve the overall QoE as compared to
fixed rule ABR algorithms. However, a common issue in the A3C methods is the
lag between behaviour policy and target policy. As a result, the behaviour and
the target policies are no longer synchronized which results in suboptimal
updates. In this work, we present ALISA: An Actor-Learner Architecture with
Importance Sampling for efficient learning in ABR algorithms. ALISA
incorporates importance sampling weights to give more weightage to relevant
experience to address the lag issues with the existing A3C methods. We present
the design and implementation of ALISA, and compare its performance to
state-of-the-art video rate adaptation algorithms including vanilla A3C
implemented in the Pensieve framework and other fixed-rule schedulers like BB,
BOLA, and RB. Our results show that ALISA improves average QoE by up to 25%-48%
higher average QoE than Pensieve, and even more when compared to fixed-rule
schedulers.Comment: Number of pages: 10, Number of figures: 9, Conference name: 24th IEEE
International Symposium on a World of Wireless, Mobile and Multimedia
Networks (WoWMoM
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