Mobile Volumetric Video Streaming Enhanced by Super Resolution

Zhang, Anlan; Wang, Chendong; Liu, Xing; Han, Bo; Qian, Feng

doi:10.1145/3386901.3396598

Cited by 16 publications

(18 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16, we add 3-bit check data to every 4-bit data and these 3-bit check data can correct 1-bit error data. The Hamming Code (7,4) has the capability of error detection rate of 1 , which is sufficient to handle the errors of the backscatter signal.…”

Section: A Link Codingmentioning

confidence: 99%

“…First, the backscatter data is divided into groups. Each group of 4-bit data is encoded by Hamming Code (7,4) and is transformed to a 7-bit codeword. An interleaver is between the Hamming encoder and the OOK modulator, which writes in columns and reads out in rows.…”

Section: B Interleavementioning

confidence: 99%

“…However, industrial applications have diverse requirements on data exchanging and thus demand different M2M communication technologies. For example, video surveillance on industrial practices requires high-throughput (tens of Gbps) M2M links to ensure reduced communication latency [7]. Keeping track of the status of machines in the factory (e.g., vibration, noise, rotation), on the other hand, demands moderate-throughput (hundreds of Kbps) M2M links for sensing data forwarding [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Ambient LoRa Backscatter With On-Off Keying Modulation

Guo

Shangguan

et al. 2022

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-ofthe-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5-199.4 Kbps data rate at various distances, 10.4-52.4× higher than PLoRa.

show abstract

Section: A Link Codingmentioning

confidence: 99%

Section: B Interleavementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Ambient LoRa Backscatter With On-Off Keying Modulation

Guo

Shangguan

et al. 2022

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

show abstract

“…The core idea is to leverage DNN's overfitting property and use the training accuracy to deliver high enhancement performance. Many following works are proposed to apply the idea of NAS to different applications, like UAV video streaming [30], Live Streaming [16], 360 Video Streaming [7,4], Volumetric Video Streaming [34], and Mobile Video Streaming [32], etc. In this paper, the proposed method can further reduce the bandwidth budget by sharing most of the parameters over video chunks.…”

Section: Dnn-based Video Super-resolutionmentioning

confidence: 99%

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation

Liu

Ming

Chen³

et al. 2021

Preprint

View full text Add to dashboard Cite

Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality of video delivery recently. These methods divide a video into chunks, and stream LR video chunks and corresponding content-aware models to the client. The client runs the inference of models to super-resolve the LR chunks. Consequently, a large number of models are streamed in order to deliver a video. In this paper, we first carefully study the relation between models of different chunks, then we tactfully design a joint training framework along with the Content-aware Feature Modulation (CaFM) layer to compress these models for neural video delivery. With our method, each video chunk only requires less than 1% of original parameters to be streamed, achieving even better SR performance. We conduct extensive experiments across various SR backbones, video time length, and scaling factors to demonstrate the advantages of our method. Besides, our method can be also viewed as a new approach of video coding. Our primary experiments achieve better video quality compared with the commercial H.264 and H.265 standard under the same storage cost, showing the great potential of the proposed method. Code is available at:https: //github.com/Neural-video-delivery/ CaFM-Pytorch-ICCV2021 * Equal Contribution. † This work was done when Jiaming Liu was an intern at Intel Labs China supervised by Ming Lu ‡ Chuang Zhang is responsible for correspondence.

show abstract

“…New standards have been released Lafruit et al (2019) with the purpose of 3D static and dynamic visual content compression for immersive reality multimedia services (360°video with head-mounted displays and free navigation in 3D space with head-mounted and 3D light field displays). Augmented reality streaming toward a mobile device represents typical challenges discussed in Lai et al (2019) and Chakareski (2020); the quality of experience is related to the visual content Han et al (2020), Zhang et al (2020), rate adaptation Hosseini and Timmerer (2018), Liu et al (2020), and device power consumption Cheng (2020).…”

Section: Introductionmentioning

confidence: 99%

Delivering Resources for Augmented Reality by UAVs: a Reinforcement Learning Approach

et al. 2021

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are supposed to be used to provide different services from video surveillance to communication facilities during critical and high-demanding scenarios. Augmented reality streaming services are especially demanding in terms of required throughput, computing resources at the user device, as well as user data collection for advanced applications, for example, location-based or interactive ones. This work is focused on the experimental utilization of a framework adopting reinforcement learning (RL) approaches to define the paths crossed by UAVs in delivering resources for augmented reality services. We develop an OpenAI Gym-based simulator that is tuned and tested to study the behavior of UAVs trained with RL to fly around a given area and serve augmented reality users. We provide abstractions for the environment, the UAVs, the users, and their requests. A reward function is then defined to encompass several quality-of-experience parameters. We train our agents and observe how they behave as a function of the number of UAVs and users at different hours of the day.

show abstract

Mobile Volumetric Video Streaming Enhanced by Super Resolution

Cited by 16 publications

References 1 publication

Efficient Ambient LoRa Backscatter With On-Off Keying Modulation

Efficient Ambient LoRa Backscatter With On-Off Keying Modulation

Overfitting the Data: Compact Neural Video Delivery via Content-aware Feature Modulation

Delivering Resources for Augmented Reality by UAVs: a Reinforcement Learning Approach

Contact Info

Product

Resources

About