The simulation and evaluation of diverse 360° video streaming systems present inherent challenges due to the varied design objectives, streaming strategies, and evaluation metrics. This poster introduces E3PO, a versatile and extensible simulation and evaluation platform for 360° video streaming systems. E3PO excels in simulating all proposed variations of 360° video streaming methods, while also generating the precise view that users experience. We have implemented E3PO and developed a variety of examples that simulate different streaming approaches. The promising results from our evaluation of these simulated scenarios demonstrate E3PO's substantial potential to assist researchers in testing new designs, fine-tuning parameters, and comparing performance with their counterparts.

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions. In LensCast, a transmission distortion minimization problem is formulated with the consideration of video chunk scheduling, path matching and power allocation, which is an intractable mixed integer non-linear programming (MINLP) problem. The solution of this MINLP problem is converted into resource allocation (i.e., joint path matching and power allocation) plus chunk scheduling. First, resource allocation is investigated with given chunk scheduling results. By analyzing the optimality of the resource allocation problem, a winner-takes-all assignment is obtained to guide resource allocation. After that, a greedy water-filling algorithm is proposed as a near-optimal solution. Second, we propose a low-complexity chunk scheduling algorithm to schedule chunks for each transmission. Simulation results demonstrate that the proposed LensCast achieves an improved performance in terms of both peak signal-to-noise ratio and visual quality comparing with reference schemes.

Intelligent reflecting surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider a multi-IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable non-convex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 147% higher sum rate compared with the benchmark and 116% higher energy efficiency compared with amplify-and-forward relay.

Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios.

Recently, pseudo-analog transmission based on SoftCast has been proposed to improve the received quality of video/image by eliminating the cliff effect in traditional digital transmission. In this paper, we propose a Compressed Pseudo-analog Transmission System (ComPaTS) for remote sensing images over bandwidth-constrained wireless channels. This novel scheme is developed based on the observation that the inherent dropping strategy in pseudo-analog transmission is impractical for remote sensing images in which the transmission bandwidth is generally insufficient. In ComPaTS, to guarantee the content diversity gain under pseudo-analog transmission, block-based Compressive Sensing (CS) is applied to the wavelet domain of each remote sensing image, where the sampling ratio is proportional to the importance of different blocks. The main work of ComPaTS is to leverage the sampling ratio in block-based CS and the resource allocation in pseudo-analog transmission in order to minimize system distortion. Two components of system distortion, i.e., source distortion and channel distortion are analyzed respectively. To characterize the coupling relationship between these two different types of distortion, a joint bandwidth-power distortion optimization problem is formulated. Furthermore, we also propose a two-stage allocation algorithm to solve the problem efficiently. The simulation results demonstrate that the proposed ComPaTS scheme significantly outperforms reference schemes in terms of peak signal-to-noise ratio under different bandwidth-constrained scenarios.

To cope with rapid growth of video traffic and energy consumption in mobile networks, we propose a spatial modulation (SM) and non-orthogonal multiple access (NOMA) integrated system for multi-user video transmission (SNIS-MUVT). SNIS-MUVT attempts to exploit the benefits from SM and NOMA to achieve high energy efficiency and high throughput. The main contribution of SNIS-MUVT lies in cross-layer design for video-aware transmission and quality of experience (QoE)-driven power allocation. With scalable video coding, base layers (BLs) are transmitted in the spatial domain while enhancement layers (ELs) are transmitted in the signal domain. In the spatial domain, layered SM is performed to support multi-user transmission according to the required BL rates and channel conditions of different users. In the signal domain, ELs are first extracted based on the estimated channel capacity. Then, the extracted ELs are handled by unequal error protection according to their importance before superposed for NOMA transmission. We formulate the QoE-driven power allocation problem in SNIS-MUVT. Both optimal algorithm based on the hidden monotonicity of the problem and suboptimal iterative algorithm with polynomial time are proposed. Finally, simulation results demonstrate that the proposed SNIS-MUVT outperforms existing SM and SM-NOMA integrated schemes in terms of QoE under various scenarios.

Recently, a novel uncoded transmission system called SoftCast has attracted great interest from academia, which can eliminate the cliff effect in digital transmission. Since the whole system is linear, SoftCast is intrinsically scalable to channel conditions. However, the resolution heterogeneity in the uncoded video broadcast system has not been well studied as far as we know. In this paper, we propose a Spatial Scalability Enabled Soft Video Broadcast (SSESVB) system based on SoftCast, aim to accommodate diverse users with heterogeneous channel conditions and heterogeneous resolutions simultaneously. Existing solutions for the resolution heterogeneity issue are previously designed for digital source coding, which cannot fully exploit performance gains from uncoded transmission. Instead, in the proposed SSESVB system, a novel spatial decomposition method based on linear projection is specially designed for uncoded transmission. Then a power distortion optimization problem is formulated and solved with the goal to minimize the average distortion of multiple users with heterogeneous channel conditions and heterogeneous resolutions. The simulation results demonstrate that SSESVB achieves more than 5.8 dB gain over compared schemes (i.e., SoftCast, Discrete Wavelet Transform (DWT) based method, and Scalable Video Coding (SVC)) in terms of average Peak Signal-to-Noise Ratio (PSNR) under heterogeneous scenarios.