2,401 research outputs found
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360-Degree Video Streaming
360-degree videos require significant bandwidth to provide an immersive
viewing experience. Wireless systems using terahertz (THz) frequency band can
meet this high data rate demand. However, self-blockage is a challenge in such
systems. To ensure reliable transmission, this paper explores THz-enabled
360-degree video streaming through multiple multi-antenna access points (APs).
Guaranteeing users' quality of experience (QoE) requires accurate viewport
prediction to determine which video tiles to send, followed by asynchronous
bitrate selection for those tiles and beamforming design at the APs. To address
users' privacy and data heterogeneity, we propose a content-based viewport
prediction framework, wherein users' head movement prediction models are
trained using a personalized federated learning algorithm. To address
asynchronous decision-making for tile bitrates and dynamic THz link
connections, we formulate the optimization of bitrate selection and beamforming
as a macro-action decentralized partially observable Markov decision process
(MacDec-POMDP) problem. To efficiently tackle this problem for multiple users,
we develop two deep reinforcement learning (DRL) algorithms based on
multi-agent actor-critic methods and propose a hierarchical learning framework
to train the actor and critic networks. Experimental results show that our
proposed approach provides a higher QoE when compared with three benchmark
algorithms.Comment: 14 pages, 11 figures. This paper has been submitted to an IEEE
journal for possible publicatio
Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks
This paper investigates the cross-layer optimization of fast video delivery
and caching for minimization of the overall video delivery time in a two-hop
relaying network. The half-duplex relay nodes are equipped with both a cache
and a buffer which facilitate joint scheduling of fetching and delivery to
exploit the channel diversity for improving the overall delivery performance.
The fast delivery control is formulated as a two-stage functional non-convex
optimization problem. By exploiting the underlying convex and quasi-convex
structures, the problem can be solved exactly and efficiently by the developed
algorithm. Simulation results show that significant caching and buffering gains
can be achieved with the proposed framework, which translates into a reduction
of the overall video delivery time. Besides, a trade-off between caching and
buffering gains is unveiled.Comment: 7 pages, 4 figures; accepted for presentation at IEEE Globecom, San
Diego, CA, Dec. 201
Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming
The growing demand for virtual reality (VR) applications requires wireless
systems to provide a high transmission rate to support 360-degree video
streaming to multiple users simultaneously. In this paper, we propose an
intelligent reflecting surface (IRS)-aided rate-splitting (RS) VR streaming
system. In the proposed system, RS facilitates the exploitation of the shared
interests of the users in VR streaming, and IRS creates additional propagation
channels to support the transmission of high-resolution 360-degree videos. IRS
also enhances the capability to mitigate the performance bottleneck caused by
the requirement that all RS users have to be able to decode the common message.
We formulate an optimization problem for maximization of the achievable bitrate
of the 360-degree video subject to the quality-of-service (QoS) constraints of
the users. We propose a deep deterministic policy gradient with imitation
learning (Deep-GRAIL) algorithm, in which we leverage deep reinforcement
learning (DRL) and the hidden convexity of the formulated problem to optimize
the IRS phase shifts, RS parameters, beamforming vectors, and bitrate selection
of the 360-degree video tiles. We also propose RavNet, which is a deep neural
network customized for the policy learning in our Deep-GRAIL algorithm.
Performance evaluation based on a real-world VR streaming dataset shows that
the proposed IRS-aided RS VR streaming system outperforms several baseline
schemes in terms of system sum-rate, achievable bitrate of the 360-degree
videos, and online execution runtime. Our results also reveal the respective
performance gains obtained from RS and IRS for improving the QoS in multiuser
VR streaming systems.Comment: 20 pages, 12 figures. This paper has been submitted to IEEE journal
for possible publicatio
Cache-Aided Non-Orthogonal Multiple Access
In this paper, we propose a novel joint caching and non-orthogonal multiple
access (NOMA) scheme to facilitate advanced downlink transmission for next
generation cellular networks. In addition to reaping the conventional
advantages of caching and NOMA transmission, the proposed cache-aided NOMA
scheme also exploits cached data for interference cancellation which is not
possible with separate caching and NOMA transmission designs. Furthermore, as
caching can help to reduce the residual interference power, several decoding
orders are feasible at the receivers, and these decoding orders can be flexibly
selected for performance optimization. We characterize the achievable rate
region of cache-aided NOMA and investigate its benefits for minimizing the time
required to complete video file delivery. Our simulation results reveal that,
compared to several baseline schemes, the proposed cache-aided NOMA scheme
significantly expands the achievable rate region for downlink transmission,
which translates into substantially reduced file delivery times.Comment: Accepted for presentation at IEEE ICC 201
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