Panoramic virtual reality (VR) video (PVRV) is becoming increasingly popular since it offers a true immersive experience. However, the ultra-high resolution of PVRV requires significant bandwidth and ultra-low latency for PVRV streaming, something that makes challenging the extension of this application to mobile networks. Besides bandwidth, the frequent perspective viewport rendering induces a heavy computational load on battery-constrained mobile devices. To attack these problems jointly, this paper proposes a PVRV streaming system that is designed for modern multi-connectivity-based (MC-based) millimeter wave (mmWave) cellular networks in conjunction with mobile edge computing (MEC). First, mmWave is deployed to support the high bandwidth needs of PVRV streaming. Next, the multiple mmWave links that tend to suffer from outages are coupled with a sub-6 GHz link to ensure disruption-free wireless communication. With the help of an MEC server, the trade-off among link adaptation, transcoding-based chunk quality adaptation and viewport rendering offloading is sought to improve the wireless bandwidth utilization and mobile device's energy efficiency. Simulation results show that the proposed scheme can improve the streaming performance in both energy efficiency and the quality of received viewport over the state-of-the-art schemes.

Delivering high fidelity virtual reality (VR) video over mobile networks is very challenging since VR applications usually require very high bandwidth and ultra low latency. With the evolution of 5G mobile networks, multi-cell multi-access edge computing (MEC) networks enable low latency data communication. However, even in this setting, the requirements of VR applications are tough to meet. To optimize the end-to-end latency for VR video delivery over multi-cell MEC networks, we propose a rendering-aware tile caching scheme. As a first step we propose collaborative tile caching in 5G MEC networks by enabling multiple cell sites to share caches so that the network caching performance is improved. Hence, the amount of redundant data delivered and eventually the latency are both significantly reduced. Second, our scheme offloads viewport rendering of VR video to the MEC server and closely couples this with cache placement, allowing thus the rendering-induced latency to be reduced. Finally, a low-delay request routing algorithm is integrated with the proposed cache placement scheme to further optimize the end-to-end latency of VR video delivery. Extensive simulation results show that the proposed rendering-aware caching scheme can achieve better latency performance than the state-of-the-art decoupled caching/rendering schemes.

This paper presents a novel rate control technique for multiview video plus depth (MVD) based 3D video coding. In the proposed rate control technique, an image-stitching method is first utilized to simultaneously encode video and depth, and then a joint rate control algorithm for MVD is presented. The joint rate control algorithm is performed on three levels, namely view level, video/depth level and frame level. In the view level, different proportions of rates are allocated for different types of views according to the pre-statistical rate allocation. In the video/depth level, the target rates for video and depth are discriminatorily assigned to guarantee the high quality of video for the backward-compatible display. In the frame level, the hierarchical rate allocation is used to regulate the target bits for each frame. In addition to the above mentioned rate control strategies, according to the special characteristics of multiview hypothetical reference decoder (HRD), the buffer-related rate control is also considered to prevent the decoder buffer from overflow or underflow even outputting multiple views. Experimental results show that the proposed rate control technique can accurately control the bit-rate to satisfy the requirements of 3D video systems.

This paper presents a replay attack that can compromise the functionality of joint radar communication (JRC) systems that use orthogonal frequency division multiplexing (OFDM) by creating ghost target signatures. The attack is particularly dangerous in connected autonomous vehicles that deploy JRC. When the attacker detects the OFDM JRC transmission it retransmits the same frame (preamble and data) but with a frequency and phase shift that varies across subcarriers and successive OFDM symbols of the preamble. This technique exploits the operation of the OFDM RADAR receiver algorithm and generates additional ghost targets in the Range-Doppler response at the JRC system. To evaluate the impact of this attack we calculate the Range-Doppler response for different system configurations of an 802.11-based OFDM JRC system and different spoofing strategies in a representative vehicular system. Our results show that JRC based on OFDM can easily be spoofed and it may lead to inability of a autonomous vehicular system to operate safely.

Global mobile video traffic is predicted to grow explosively in the near future and high-definition (HD) video streaming applications are increasingly prevailing over the mobile Internet. To optimize the transmission performance of HD video streaming applications over the heterogeneous wireless access networks, multipath video streaming that exploits the multihoming features of mobile devices is proposed as a potential solution. However, the delivery of HD video has much higher bitrate and more stringent delay constraint than other services. It is thus challenging to transfer HD video through wireless networks. Conventionally, multipath video streaming is optimized by the video distortions or other network-level quality-of-service parameters, which do not correlate well with the human visual perceptions. To address this problem, we first propose a novel quality-of-experience (QoE) prediction model to prefer the QoE values of different rate allocation scenarios over heterogeneous wireless access networks. Then, the rate allocation problem over heterogeneous wireless networks is formulated as a nonlinear optimization problem of maximizing the perceived video quality at the receiver. A relaxing function called penalty function is exploited to transform the rate allocation problem into an unconstrained optimizing problem and the pattern search method is adopted to obtain the close-to-optimal solution in each rate allocation session. The extensive simulation results show that our proposal significantly improves the QoE in terms of mean opinion score compared with the reference schemes.