Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video
Streaming over HTTP
- URL: http://arxiv.org/abs/1912.11954v1
- Date: Fri, 27 Dec 2019 01:19:14 GMT
- Title: Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video
Streaming over HTTP
- Authors: Hui Yuan, Huayong Fu, Ju Liu, Junhui Hou, and Sam Kwong
- Abstract summary: Dynamic Adaptive Streaming over HTTP (DASH) has demonstrated to be an emerging and promising multimedia streaming technique.
We propose a novel algorithm to optimally allocate the limited export bandwidth of the server to multi-users to maximize their Quality of Experience (QoE) with fairness guaranteed.
- Score: 89.30855958779425
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dynamic Adaptive Streaming over HTTP (DASH) has demonstrated to be an
emerging and promising multimedia streaming technique, owing to its capability
of dealing with the variability of networks. Rate adaptation mechanism, a
challenging and open issue, plays an important role in DASH based systems since
it affects Quality of Experience (QoE) of users, network utilization, etc. In
this paper, based on non-cooperative game theory, we propose a novel algorithm
to optimally allocate the limited export bandwidth of the server to multi-users
to maximize their QoE with fairness guaranteed. The proposed algorithm is
proxy-free. Specifically, a novel user QoE model is derived by taking a variety
of factors into account, like the received video quality, the reference buffer
length, and user accumulated buffer lengths, etc. Then, the bandwidth competing
problem is formulated as a non-cooperation game with the existence of Nash
Equilibrium that is theoretically proven. Finally, a distributed iterative
algorithm with stability analysis is proposed to find the Nash Equilibrium.
Compared with state-of-the-art methods, extensive experimental results in terms
of both simulated and realistic networking scenarios demonstrate that the
proposed algorithm can produce higher QoE, and the actual buffer lengths of all
users keep nearly optimal states, i.e., moving around the reference buffer all
the time. Besides, the proposed algorithm produces no playback interruption.
Related papers
- ResQ: Residual Quantization for Video Perception [18.491197847596283]
We propose a novel quantization scheme for video networks coined as Residual Quantization.
We extend our model to dynamically adjust the bit-width proportional to the amount of changes in the video.
arXiv Detail & Related papers (2023-08-18T12:41:10Z) - Reinforcement Learning -based Adaptation and Scheduling Methods for
Multi-source DASH [1.1971219484941955]
Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently.
In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths.
This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS)
arXiv Detail & Related papers (2023-07-25T06:47:12Z) - An Adaptive Device-Edge Co-Inference Framework Based on Soft
Actor-Critic [72.35307086274912]
High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices.
We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations.
Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
arXiv Detail & Related papers (2022-01-09T09:31:50Z) - AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally
Consistent Video Semantic Segmentation [81.87943324048756]
In video segmentation, generating temporally consistent results across frames is as important as achieving frame-wise accuracy.
Existing methods rely on optical flow regularization or fine-tuning with test data to attain temporal consistency.
This paper presents an efficient, intuitive, and unsupervised online adaptation method, AuxAdapt, for improving the temporal consistency of most neural network models.
arXiv Detail & Related papers (2021-10-24T07:07:41Z) - End-to-end Neural Video Coding Using a Compound Spatiotemporal
Representation [33.54844063875569]
We propose a hybrid motion compensation (HMC) method that adaptively combines the predictions generated by two approaches.
Specifically, we generate a compoundtemporal representation (STR) through a recurrent information aggregation (RIA) module.
We further design a one-to-many decoder pipeline to generate multiple predictions from the CSTR, including vector-based resampling, adaptive kernel-based resampling, compensation mode selection maps and texture enhancements.
arXiv Detail & Related papers (2021-08-05T19:43:32Z) - ANT: Learning Accurate Network Throughput for Better Adaptive Video
Streaming [20.544139447901113]
Adaptive Bit Rate (ABR) decision plays a crucial role for ensuring satisfactory Quality of Experience (QoE) in video streaming applications.
This paper proposes to learn the ANT (a.k.a., Accurate Network Throughput) model to characterize the full spectrum of network throughput dynamics in the past.
Experiment results show that our approach can significantly improve the user QoE by 65.5% and 31.3% respectively, compared with the state-of-the-art Pensive and Oboe.
arXiv Detail & Related papers (2021-04-26T12:15:53Z) - Phase Retrieval using Expectation Consistent Signal Recovery Algorithm
based on Hypernetwork [73.94896986868146]
Phase retrieval is an important component in modern computational imaging systems.
Recent advances in deep learning have opened up a new possibility for robust and fast PR.
We develop a novel framework for deep unfolding to overcome the existing limitations.
arXiv Detail & Related papers (2021-01-12T08:36:23Z) - A Deep-Unfolded Reference-Based RPCA Network For Video
Foreground-Background Separation [86.35434065681925]
This paper proposes a new deep-unfolding-based network design for the problem of Robust Principal Component Analysis (RPCA)
Unlike existing designs, our approach focuses on modeling the temporal correlation between the sparse representations of consecutive video frames.
Experimentation using the moving MNIST dataset shows that the proposed network outperforms a recently proposed state-of-the-art RPCA network in the task of video foreground-background separation.
arXiv Detail & Related papers (2020-10-02T11:40:09Z) - Real-world Video Adaptation with Reinforcement Learning [38.26695924173461]
Client-side video players employ adaptive (ABR) algorithms to optimize user quality of experience (QoE)
We evaluate recently proposed RL-based ABR methods in Facebook's web-based video streaming platform.
In a week-long worldwide deployment with more than 30 million video streaming sessions, our RL approach outperforms the existing human-engineered ABR algorithms.
arXiv Detail & Related papers (2020-08-28T21:44:24Z) - Rethinking Differentiable Search for Mixed-Precision Neural Networks [83.55785779504868]
Low-precision networks with weights and activations quantized to low bit-width are widely used to accelerate inference on edge devices.
Current solutions are uniform, using identical bit-width for all filters.
This fails to account for the different sensitivities of different filters and is suboptimal.
Mixed-precision networks address this problem, by tuning the bit-width to individual filter requirements.
arXiv Detail & Related papers (2020-04-13T07:02:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.