Video quality

About: Video quality is a research topic. Over the lifetime, 13143 publications have been published within this topic receiving 178307 citations.

An integrated source coding and congestion control framework for video streaming in the Internet

Abstract: We describe a framework for video transmission over the Internet that features the coordinated operation of an application-layer video source coding algorithm and a transport-layer rate control mechanism. The proposed video coding scheme operates on a progressively encoded video stream and provides graceful resilience to network packet drops. The robustness is enabled through a generalized multiple description (MD) coding strategy, architectured as an adaptive array of packet-erasure correction codes. The video coding algorithm is matched to an efficient and reactive rate control mechanism that minimizes the fluctuation of rate and uses the profile of past losses to adjust the rate in a TCP-friendly manner. While the two constituent algorithms identified above are interesting in their own right, a key feature of this work is the integration of these algorithms in a simple framework that seeks to maximize the expected delivered video quality at the receiver through coordinated adaptation of the two components. We present simple simulation results to illustrate the utility of our approach.

QoE-Driven Channel Allocation and Handoff Management for Seamless Multimedia in Cognitive 5G Cellular Networks

Abstract: Cognitive radio (CR) is among the promising solutions for overcoming the spectrum scarcity problem in the forthcoming fifth-generation (5G) cellular networks, whereas mobile stations are expected to support multimode operations to maintain connectivity to various radio access points. However, particularly for multimedia services, because of the time-varying channel capacity, the random arrivals of legacy users, and the on-negligible delay caused by spectrum handoff, it is challenging to achieve seamless streaming leading to minimum quality of experience (QoE) degradation. The objective of this paper is to manage spectrum handoff delays by allocating channels based on the user QoE expectations, minimizing the latency, providing seamless multimedia service, and improving QoE. First, to minimize the handoff delays, we use channel usage statistical information to compute the channel quality. Based on this, the cognitive base station maintains a ranking index of the available channels to facilitate the cognitive mobile stations. Second, to enhance channel utilization, we develop a priority-based channel allocation scheme to assign channels to the mobile stations based on their QoE requirements. Third, to minimize handoff delays, we employ the hidden markov model (HMM) to predict the state of the future time slot. However, due to sensing errors, the scheme proactively performs spectrum sensing and reactively acts handoffs. Fourth, we propose a handoff management technique to overcome the interruptions caused by the handoff. In such a way that, when a handoff is predicted, we use scalable video coding to extract the base layer and transmit it during a certain interval time before handoff occurrence to be shown during handoff delays, hence providing seamless service. Our simulation results highlight the performance gain of the proposed framework in terms of channel utilization and received video quality.

Optimal quality adaptation for MPEG-4 fine-grained scalable video

Abstract: Dynamic behavior of the Internet's transmission resources makes it difficult to provide perceptually good quality of streaming video. MPEG-4 Fine-Grained Scalable coding is proposed to deal with this problem by distributing the data in enhancement layers over a wide range of bit rates. However, encoded video also exhibits significant data rate variability to provide a consistent quality video. We are, therefore, faced with the problem of trying to accommodate the mismatch between the available bandwidth variability and the encoded video variability. In this paper, we investigate quality adaptation of the layered VBR video generated by MPEG-4 FGS. Our goal is to develop a quality adaptation scheme that maximizes perceptual video quality through minimizing quality variation while at the same time increasing the usage of available bandwidth. We develop an optimal adaptation scheme and an online heuristic based on whether the network conditions are known a priori. Experimental results show that the online heuristic as well as the optimal adaptation algorithm provide consistent video quality when used over both TFRC and TCP.

OLM: Opportunistic Layered Multicasting for Scalable IPTV over Mobile WiMAX

Abstract: We propose Opportunistic Layered Multicasting (OLM), a joint user scheduling and resource allocation algorithm that provides enhanced quality and efficiency for layered video multicast over Mobile WiMAX. This work is a lead off and complete synergy of layered video multicasting with opportunistic concept. The target application is characterized by groups of users acquiring popular video programs over a fading channel. To accommodate various bandwidth requirements and device capability, video streams are coded into base and enhancement layers using scalable video coding technology. Correspondingly, the optimization problems, which select the best subset of users to receive a specific video layer and assign the most appropriate modulation and coding scheme for this video layer, are specifically formulated for both video layer types. We also design fast and effective algorithms to bridge the gap between theoretical throughput capacity and implementation concerns. Thus, the basic video quality can be efficiently guaranteed to all subscribers while creating most utility out of limited resources on enhancement information. To overcome the inevitable packet loss in a multicast session, an FEC rate adaptation scheme to approach theoretical performance is also presented. Favorable performance of the proposed algorithms is demonstrated by simulations utilizing realistic Mobile WiMAX parameters.

“To pool or not to pool”: A comparison of temporal pooling methods for HTTP adaptive video streaming

Abstract: Current objective video quality metrics typically estimate video quality for short video sequences (10 to 15 sec) of constant quality. However, customers of video services usually watch longer sequences of videos which are more and more delivered via adaptive streaming methods such as HTTP adaptive streaming (HAS). A viewing session in such a setting contains several different video qualities over time. In order to express this in an overall score for the whole viewing session, several temporal pooling methods have been proposed in the related work. Within this paper, we set out to compare the performance of different temporal pooling methods for the prediction of Quality of Experience (QoE) for HTTP video streams with varying qualities. We perform this comparison based on ground truth rating data gathered in a crowdsourcing study in the context of the NGMN P-SERQU project. As input data for the models, we use objective video quality metrics such as PSNR, SSIM but also very basic inputs such as the bitrate of the clips only. Our results show that certain pooling methods perform clearly better than others. These results can help in identifying well performing temporal pooling methods in the context of HAS.