The structured nature of video data motivates introducing video-aware decisions that make use of this structure for improved video transmission over wireless networks . In this paper, we introduce an architecture for real-time video transmission over multiple-input multiple-output (MIMO) wireless communication systems using loss visibility side information . We quantify the perceptual importance of a packet through the packet loss visibility and use the loss visibility distribution to provide a notion of relative packet importance . To jointly achieve high video quality and low latency, we define the optimization objective function as the throughput weighted by the loss visibility of each packet, a proxy for the total perceptual value of successful packets per unit of time. We solve the problem of mapping video packets to MIMO subchannels and adapting per-stream rates to maximize the proposed objective . We show that the solution enables jointly reaping gains in terms of improved video quality and lower latency. Optimized packet-stream mapping enables transmission of more relevant packets over more reliable streams while unequal modulation opportunistically increases the transmission rate on the stronger streams to enable low latency delivery of high priority packets. Tested on H.264-encoded video sequences, for a 4 ×4 MIMO system with three spatial streams, the proposed architecture achieves 8 dB power reduction for the same video quality and supports 2.4× higher throughput due to unequal modulation. Furthermore, the gains are achieved at the expense of few bits of cross-layer overhead rather than a complex cross-layer design.

Dual-polarized antennas provide a mechanism to achieve polarization diversity or multiplex non-interfering data streams over-the-air. Practical realization of dual-polarized transmission, however, must overcome challenges such as mobile misorientation with respect to the base station as well as non-ideal polarization isolation. With the advent of mmWave communications, dual-polarized large scale antenna arrays can be realized inexpensively. In this paper, we propose a beamforming algorithm that jointly designs the horizontal and vertical beamformers and combiners to maximize spectral efficiency in the presence of mobile misorientation and polarization leakage. First, we show that dual-polarized antenna arrays introduce channel structure that enables mobile orientation estimation without explicit channel knowledge. We derive the maximum likelihood estimate of the azimuth and elevation mobile rotation. Given these estimates, we design the horizontal and vertical beamformer and combiner with practical RF hardware constraints. We generalize the beamforming algorithm to enable multiplexing multiple data streams across different channel paths in the horizontal and vertical domain. Results show that mobile orientation estimation using dual-polarized antenna arrays achieves over 90% accuracy even below 0 dB. Using a realistic clustered channel model, spectral efficiency gains of the order of 5 dB are demonstrated over dual-polarized antennas with independent horizontal/vertical beam steering as well as single polarized antenna arrays.

Real-time video demands quality-of-service (QoS) guarantees such as delay bounds for end-user satisfaction. Due to the stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. Instead, this paper proposes providing statistical delay guarantees using the concept of effective capacity. A multiuser setup is considered whereby different users have (possibly different) delay QoS constraints. The resource allocation policy that maximizes the sum video quality is derived and has a useful intuitive interpretation: The optimal operating point per user is such that the rate-distortion slope is the inverse of the supported video source rate per unit bandwidth, termed source spectral efficiency. Scheduling policies are also proposed to select a maximal user subset such that all selected users can meet their statistical delay requirement. Results show that QoS-aware scheduling and resource allocation enable supporting significantly more users under the same resource constraints.

Delivering high perceptual quality video over wireless channels is challenging due to the changing channel quality and the variations in the importance of one source packet to the next for the end-user's perceptual experience. Leveraging perceptual metrics in concert with link adaptation to maximize perceptual quality and satisfy real-time delay constraints is largely unexplored. We introduce an APP/MAC/PHY cross-layer architecture that enables optimizing perceptual quality for delay-constrained scalable video transmission. We propose an online QoS-to-QoE mapping technique to quantify the loss visibility of packets from each video layer using the ACK history and perceptual metrics. At the PHY layer, we develop a link adaptation technique that uses the QoS-to-QoE mapping to provide perceptually-optimized unequal error protection per layer according to packet loss visibility. At the APP layer, the source rate is adapted by selecting the set of temporal and quality layers to be transmitted based on the channel statistics, source rates, and playback buffer state. The proposed cross-layer optimization framework allows the channel to adapt at a faster time scale than the video codec. Furthermore, it provides a tradeoff between playback buffer occupancy and perceptual quality. We show that the proposed architecture prevents playback buffer starvation, provides immunity against short-term channel fluctuations, regulates the buffer size, and achieves a 30% increase in video capacity versus throughput-optimal link adaptation.

For real-time video broadcast where multiple users are interested in the same content, mobile-to-mobile cooperation can be utilized to improve delivery efficiency and reduce network utilization. Under such cooperation, however, real-time video transmission requires end-to-end delay bounds. Due to the inherently stochastic nature of wireless fading channels, deterministic delay bounds are prohibitively difficult to guarantee. For a scalable video structure, an alternative is to provide statistical guarantees using the concept of effective capacity/bandwidth by deriving quality of service exponents for each video layer. Using this concept, we formulate the resource allocation problem for general multihop multicast network flows and derive the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. A method is described to compute the optimal resource allocation at each node in a distributed fashion. Furthermore, we propose low complexity approximation algorithms for energy-efficient flow selection from the set of directed acyclic graphs forming the candidate network flows. The flow selection and resource allocation process is adapted for each video frame according to the channel conditions on the network links. Considering different network topologies, results demonstrate that the proposed resource allocation and flow selection algorithms provide notable performance gains with small optimality gaps at a low computational cost.

Providing perceptual quality guarantees for video transmission over wireless channels is important for the end-user experience. The paper proposes an algorithm for perceptual quality optimization of scalable H.264 video with temporal and quality scalability. The algorithm supports adaptive unequal error protection so that different video layers are protected according to their relevance to video quality. For each video layer, a target packet error rate (PER) is selected such that a perceptual quality guarantee, measured using the multi-scale structural similarity (MS-SSIM) index, is satisfied. Given the target PER per video layer, the algorithm selects the modulation and coding scheme and the number of temporal and quality layers to transmit adaptively based on the channel state information (CSI) and source rates per video layer. Results show that the algorithm provides immunity against short term channel fluctuations, balances reliability and perceptual quality, reduces playback buffer starvation probability, and provides a convenient buffer management policy.