Perceptual QoE-Optimal Resource Allocation for Adaptive Video Streaming

Eswara, Nagabhushan; Chakraborty, Soumen; Sethuram, Hemanth P.; Kuchi, Kiran; Kumar, Abhinav; Channappayya, Sumohana S.

doi:10.1109/tbc.2019.2954064

Cited by 12 publications

(5 citation statements)

References 52 publications

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“…However, this method only adapts video rate at each node individually, and it is difficult to achieve overall clients bitrate adaption optimization without coordinating with each other. Eswara et al [21] formulated the resource allocation problem for adaptive streaming as a stochastic optimization problem with the purpose to optimize the long term QoE metrics. However, the formulated problem treats each flow individually and ignores the multicast feature of the wireless communication, decreasing the transmission performance.…”

Section: B Related Workmentioning

confidence: 99%

“…This problem can be easily solved by considering its dual as in [21], similar to eq. ( 13), (21) where p i denotes the path used by provider i.…”

Section: Heuristic Distributed Rate Adaptation Algorithmmentioning

confidence: 99%

“…This problem can be easily solved by considering its dual as in [21], similar to eq. ( 13), (21) where p i denotes the path used by provider i. Intuitively, the inverse of bitrate is equal to waiting delay of sending unit number of data according to the little's law.…”

Section: Heuristic Distributed Rate Adaptation Algorithmmentioning

confidence: 99%

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Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Zhong

Wang

et al. 2023

IEEE Trans. on Broadcast.

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Adaptive streaming based on DASH offers personalized video experience and smooth playback by allowing dynamical adjustments of the video bitrate to the variations of network conditions. This is especially important for current and future Internet video streaming applications, including emerging ones such as virtual reality-based, as adaptive streaming plays a key role in providing high quality viewing experience, especially in limited bandwidth delivery environments. To enable this promising avenue in a 5G context, efforts are made to consider it alongside multicast and edge caching, as part of the next generation communication technology. In this paper, we model the adaptive streaming transmission problem in a mobile scenario as a multi-source multicast multi-rate problem (MMMP) whose linear relaxation is concave. We decompose the problem in terms of clients and propose the distributed delivery algorithm (DDA). The computation complexity, convergence and time-varying adaptation of DDA are theoretically analyzed. Additionally, to further reduce the computation complexity of the solution, a heuristic approximation method (H-DDA) based on the physical meaning of the problem is proposed and it is also shown how H-DDA converges to the optimal value by numerical means. Finally, we conduct a series of simulation tests to demonstrate the superiority of the proposed HDDA in comparison with other state-of-art solutions.

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Section: B Related Workmentioning

confidence: 99%

“…This problem can be easily solved by considering its dual as in [21], similar to eq. ( 13), (21) where p i denotes the path used by provider i.…”

Section: Heuristic Distributed Rate Adaptation Algorithmmentioning

confidence: 99%

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Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Zhong

Wang

et al. 2023

IEEE Trans. on Broadcast.

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“…The author [23] examines and develops HTTP adaptive streaming's processing and datatransmission capabilities for live streaming web-based video with different edge rates and objectives. In order to evaluate the viewer experience of live video channels, this system also presents an asset-aware QoE model.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

Vijayashaarathi¹,

NithyaKalyani²

2023

Computer Systems Science and Engineering

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In recent years, real-time video streaming has grown in popularity. The growing popularity of the Internet of Things (IoT) and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience (QoE) and performance objectives. Most researchers focused on Forward Error Correction (FEC) techniques when attempting to strike a balance between QoE and performance. However, as network capacity increases, the performance degrades, impacting the live visual experience. Recently, Deep Learning (DL) algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks. But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time. To address the previous challenge, this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics. The proposed framework contains modules such as Intelligent Content Extraction Module (ICEM), Channel Status Monitor (CSM), and Adaptive FEC (AFEC). This framework adopts the Cognitive Learning-based Scheduling (CLS) Module, which works on the deep Reinforced Gated Recurrent Networks (RGRN) principle and embeds them along with the FEC to achieve better performances. The complete framework was developed using the Objective Modular Network Testbed in C++ (OMNET++), Internet networking (INET), and Python 3.10, with Keras as the front end and Tensorflow 2.10 as the back end. With extensive experimentation, the proposed model outperforms the other existing intelligent models in terms of improving the QoE, minimizing the End-to-End Delay (EED), and maintaining the highest accuracy (98%) and a lower Root Mean Square Error (RMSE) value of 0.001.

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“…How long a user feels immersion in a VR video dictates the level of experience perceived by users. Accurate QoE assessment is a key factor in optimizing the adaptive video streaming [63]. However, calculating visual quality alone is not adequate for a complete VR QoE framework.…”

Section: A Cfov Qoementioning

confidence: 99%

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

Yaqoob

Muntean

2021

IEEE Trans. on Broadcast.

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Recently, 360 • or omnidirectional videos have become increasingly popular for both personal and enterprize use-cases. However, 360 • video streaming has very high bandwidth and processing requirements. State-of-the-art viewport-based streaming solutions lower these requirements by performing selective streaming based on long-term Field-of-View (FoV) prediction mechanisms. However, sometimes user movement is extremely unpredictable during some parts of the video, and applying these solutions adversely affects the overall quality of experience (QoE). This paper proposes a novel Combined Field-of-View tile-based adaptive streaming solution (CFOV) that improves end-user QoE for 360 • video streaming. CFOV performs interactive tile selection based on more accurate dynamical viewing area identification by combining the results of two FoV prediction mechanisms. It also employs an innovative priority-based bitrate adaptation approach that ensures improved bitrate budget distribution between different tiles. We evaluate the proposed solution with a comprehensive set of experiments involving four immersive videos, diverse tiling patterns (i.e., 4x3, 6x4, and 8x6), different segment lengths (i.e., 1s, 2s, and 3s), and 48 empirical head movement traces under different bandwidth settings. The evaluation employs a newly defined QoE metric specifically introduced to assess the streaming performance of 360 • videos objectively. The experimental findings show that, compared to alternative approaches, our proposed solution can achieve a higher viewport match and can significantly improve the user QoE for different watching behaviors and content characteristics.

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Perceptual QoE-Optimal Resource Allocation for Adaptive Video Streaming

Cited by 12 publications

References 52 publications

Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Decentralized Optimization for Multicast Adaptive Video Streaming in Edge Cache-Assisted Networks

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

A Combined Field-of-View Prediction-Assisted Viewport Adaptive Delivery Scheme for 360° Videos

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