Predicting Long-Term Field of View in 360-Degree Video Streaming

Dong, Pingping; Shen, Rongcheng; Xie, Xuhui; Zuo, Yuning; Zhang, Lianming

doi:10.1109/mnet.106.2100449

Cited by 5 publications

(1 citation statement)

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“…Existing FoV prediction methods are typically designed using linear regression, motion estimation, machine learning methods, user clustering, or hybrid prediction mechanisms [64]. Dong et al [178] combined the historical motion trajectory of the target user with the future trajectory of other comparable users, proposing a Gaze-based FoV prediction model to enhance the accuracy of long-term FoV prediction. Pang [176] enhanced the precision of predicting the user's future viewport by incorporating object movement with the trajectory of the user's head movement and the video saliency map.…”

Section: Adaptive Bitratementioning

confidence: 99%

A Survey on Video Streaming for Next-Generation Vehicular Networks

Huang,

Cheng,

Lien

et al. 2024

Electronics

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As assisted driving technology advances and vehicle entertainment systems rapidly develop, future vehicles will become mobile cinemas, where passengers can use various multimedia applications in the car. In recent years, the progress in multimedia technology has given rise to immersive video experiences. In addition to conventional 2D videos, 360° videos are gaining popularity, and volumetric videos, which can offer users a better immersive experience, have been discussed. However, these applications place high demands on network capabilities, leading to a dependence on next-generation wireless communication technology to address network bottlenecks. Therefore, this study provides an exhaustive overview of the latest advancements in video streaming over vehicular networks. First, we introduce related work and background knowledge, and provide an overview of recent developments in vehicular networking and video types. Next, we detail various video processing technologies, including the latest released standards. Detailed explanations are provided for network strategies and wireless communication technologies that can optimize video transmission in vehicular networks, paying special attention to the relevant literature regarding the current development of 6G technology that is applied to vehicle communication. Finally, we proposed future research directions and challenges. Building upon the technologies introduced in this paper and considering diverse applications, we suggest a suitable vehicular network architecture for next-generation video transmission.

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Section: Adaptive Bitratementioning

confidence: 99%