Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges

Jiang, Xiantao; Yu, F. Richard; Song, Tian; Leung, Victor C. M.

doi:10.1109/tits.2021.3065209

Cited by 53 publications

(33 citation statements)

References 167 publications

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“…Kim et al [24] introduced additional supervised information in a selective VQA task and proposed a multi-task ratio adjustment method to prioritize the learning tasks. Jiang et al [25] made feasible optimizations to the input video stream, save additional overhead for subsequent model extraction while ensuring the reliability and security of its transmission. Zhao et al [26] proposed a spatio-temporal attention network based on video keyframes to learn joint representation, which achieves progressive joint representation learning and improves the performance of open VQA.…”

Section: Multimodal Fusionmentioning

confidence: 99%

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A燬ystems

Zhang¹,

Liu²,

Li³

et al. 2022

Computers, Materials &Amp; Continua

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Performance of Video Question and Answer (VQA) systems relies on capturing key information of both visual images and natural language in the context to generate relevant questions' answers. However, traditional linear combinations of multimodal features focus only on shallow feature interactions, fall far short of the need of deep feature fusion. Attention mechanisms were used to perform deep fusion, but most of them can only process weight assignment of single-modal information, leading to attention imbalance for different modalities. To address above problems, we propose a novel VQA model based on Triple Multimodal feature Cyclic Fusion (TMCF) and Self-Adaptive Multimodal Balancing Mechanism (SAMB). Our model is designed to enhance complex feature interactions among multimodal features with cross-modal information balancing. In addition, TMCF and SAMB can be used as an extensible plug-in for exploring new feature combinations in the visual image domain. Extensive experiments were conducted on MSVD-QA and MSRVTT-QA datasets. The results confirm the advantages of our approach in handling multimodal tasks. Besides, we also provide analyses for ablation studies to verify the effectiveness of each proposed component.

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Section: Multimodal Fusionmentioning

confidence: 99%

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A燬ystems

Zhang¹,

Liu²,

Li³

et al. 2022

Computers, Materials &Amp; Continua

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“…A comprehensive survey on edge selection for video streaming over VANET is done by Jiang et al [2]. The work [3] proposes caching strategy using static BSs as edges and jointly optimizes the objectives of user-experienced delay and service cost minimization.…”

Section: Related Workmentioning

confidence: 99%

Cost-effective Distributed Edge vehicle selection for Multi-tier Video streaming over Vehicular network

Chowdhury

Nandi

Goswami

2022

2022 IEEE 47th Conference on Local Computer Networks (LCN)

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Video traffic demands for vehicle infotainment services have rapidly increased in recent years. To reduce bandwidth costs and delays, content distributors (CD) use edge vehicles to distribute video contents over vehicular networks. CDs usually offer multiple subscription tiers of varying video qualities for their clients. A CD's business goal is to make a minimal edge vehicle selection to maximize Internet traffic offloading while satisfying heterogeneous video quality requirements of clients belonging to different subscription tiers. This work proposes an optimum cost-effective solution for edge vehicle selection for delay-constrained live video streaming over vehicular networks with multi-tier bitrate agreements. The edge vehicle selection problem is formulated as the minimum set covering problem for optimum results, and a distributed approximation solution is proposed to increase scalability. Simulation results show that the proposed solution is cost-effective for CDs and saves significant control message overhead while satisfying delay constraints and multi-tier bitrate agreements.

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“…us, the blockchain technology can help to enable the privacy of video providers and the security of requested video data. rough the distributed storage mechanism of the blockchain, the security of video information is improved, the collection of individual video data is realized through the personal ledger of blockchain, and the authorized use of video data is realized through asymmetric encryption and public and private key design in blockchain [12][13][14][15]. It is essential to introduce the blockchain into the adaptive video services in the mobile edge computing system.…”

Section: Related Workmentioning

confidence: 99%

Blockchain-Enabled Intelligent Video Caching and Transcoding in Clustered MEC Networks

Wan

2021

Security and Communication Networks

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In recent years, the number of smart devices has exploded, leading to an unprecedented increase in demand for video live and video-on-demand (VoD) services. Also, the privacy of video providers and requesters and the security of requested video data are much more threatened. In order to solve these issues, in this paper, a blockchain-enabled CMEC video transmission model (Bl-CMEC) for intelligent video caching and transcoding will be proposed to ensure the transactions’ transparency, system security, user information privacy, and integrity of the video data, enhance the ability of severs in actively caching popular video content in the CMEC system, and realize transcoding function at network edge nodes. Furthermore, we chose a scheme based on deep reinforcement learning (DRL) to intelligently access the intracluster joint caching and transcoding decisions. Then, the joint video caching and transcoding decision smart contract is specially designed to automatically manage the transaction process of the joint caching and transcoding service, which records key information of joint caching and transcoding transactions and payment information on a continuous blockchain. The simulation results demonstrate that the proposed Bl-CMEC framework not only can provide users with better QoE performance for video streaming service but also can ensure the security, integrity, and consistency for the video providers, video requesters, and video data.

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Resource Allocation of Video Streaming Over Vehicular Networks: A Survey, Some Research Issues and Challenges

Cited by 53 publications

References 167 publications

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A燬ystems

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A燬ystems

Cost-effective Distributed Edge vehicle selection for Multi-tier Video streaming over Vehicular network

Blockchain-Enabled Intelligent Video Caching and Transcoding in Clustered MEC Networks

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