The encoding, delivery and interactive consumption of omnidirectional videos still face many challenges. Traditional encoding techniques, based on Equirectangular Projection (ERP) formats, introduce significant pixel redundancy. This has prompted the appearance of advanced solutions based on the segmentation in separate regions or tiles, and their selective delivery depending on the users' viewpoint. However, tiling techniques introduce further challenges. First, neighboring pixels are encoded separately, which may result in noticeable separations between regions. Second, they can involve synchronization problems when the users' viewpoints change. Third, they may require further extensions to existing technologies, such as Dynamic Adaptive Streaming over HTTP (DASH), which makes their adoption in current web browsers very challenging. The use of Cubemap Projection (CMP) is alternatively gaining popularity due to its advantages compared to ERP. However, it requires the streaming of the whole 360° area. This paper proposes a novel tiled Cubemap (TiCMP) strategy that overcomes all the mentioned limitations. TiCMP is based on dividing the cube into two tiles, adaptively streaming them based on the users' viewpoint, and playing them out in a synchronized manner in web-based players. Evaluation results demonstrate that TiCMP provides significant bandwidth savings, without negatively impacting the Quality of Experience (QoE) when compared to traditional Equirectangular- and Cubemap-based strategies.

Dynamically adaptive streaming over HTTP (DASH), the most common streaming technique, requires a video server to store all the transcoded versions, resulting in a lot of storage space, thereby consuming a significant disk power. A disk array can be divided into hot and cold zones to allow cold disks to be spun down, but this poses several questions such as (1) which video segments can be stored on the hot disks, (2) how to allocate video segments among the hot disks, and (3) how to handle requests to the cold disks. To address this, we propose three new algorithms; (1) a hot data classification algorithm to determine which segments should be stored on the hot disks, by taking segment popularity and quality-of-experience (QoE) into account, (2) a video segment allocation algorithm to balance workloads among the hot disks, and (3) a disk bandwidth allocation algorithm which determines the bit-rate of each segment with the aim of maximizing overall QoE. Experimental results show that our scheme can reduce the power consumption between 29% and 46% compared with the method of storing all the transcoded versions at the cost of 1.5% QoE degradation.

Rate control is widely adopted during video streaming to provide both high video qualities and low latency under various network conditions. However, despite that many work have been proposed, they fail to tackle one major problem: previous methods determine a future transmission rate as a single for value which will be used in an entire time-slot, while real-world network conditions, unlike lab setup, often suffer from rapid and stochastic changes, resulting in the failures of predictions.

In this paper, we propose a delay-constrained rate control approach based on end-to-end deep learning. The proposed model predicts future bit rate not as a single value, but as possible bit rate ranges using target delay gradient, with which the transmission delay is guaranteed. We collect a large scale of real-world live streaming data to train our model, and as a result, it automatically learns the correlation between throughput and target delay gradient. We build a testbed to evaluate our approach. Compared with the state-of-the-art methods, our approach demonstrates a better performance in bandwidth utilization. In all considered scenarios, a range based rate control approach outperforms the one without range by 19% to 35% in average QoE improvement.

Video streaming traffic often dominates mobile wireless networks, forcing Internet Service Providers (ISPs) to deploy video shaping to identify and then manage traffic during congested periods Unfortunately, the increasing use of end-to-end encryption (e.g., TSL/SSL) makes it difficult to identify video flows even with deep packet inspection. As an alternative, this paper presents Silhouette -- a real-time, lightweight video classification method suitable for ISP middle-boxes. Silhouette uses only flow statistics (i.e., "shape") for video identification making it payload-agnostic, effective for identifying video flow even when encrypted. Preliminary results with pre-classified YouTube traffic shows the promise of the Silhouette approach, yielding high identification accuracy over a range of video content and encoding qualities.

Online mobile game (OMG) is booming recently, which has driven user expectations for high-quality game service. Therefore, it is crucial for game operators to understand if and how system factors (i.e. network quality metrics such as delay and mobile phone's rendering performance such as frame rate) affect gaming experience and how to optimize resource allocation to improve it. This paper is a first step towards addressing these problems. Despite the rich literature on multimedia services and Quality of Experience (QoE) measurement, the understanding of gaming experience is limited because the game platform shifts from traditional PC end to the mobile end, which has yet to be explored in depth. Based on a large-scale dataset collected from Honour of Kings, the world's top grossing mobile game, we carry out elaborate studies to explore gaming experience from the aspects of user behavior and game quality. Our key findings are as follows. First, user behavior is mainly limited to the game logic itself, such as I of a game is restricted by game rules. Therefore, it cannot well represent gaming experience. Second, among all system factors, the biggest impact on game quality comes from network performance rather than the frame rate of device, which is different from the influence mode in PC games. Third, some context factors, such as AP/BaseStations used by mobile phones, can have an indirect but huge impact on gaming experience. Based on the above observations, we provide insights that can enhance OMG's system from the perspective of resource allocation and real-time gaming experience monitoring. To the best of our knowledge, we are the first to conduct large-scale measurement to study gaming experience of OMGs in the wild. We believe our study is not only crucial for the understanding of gaming experience, but also helpful for game operators to optimize their systems.

HTTP Adaptive Streaming (HAS) is the widely adopted solution for video streaming over the Internet. When network throughput is highly variable, designing an optimal HAS solution that maximizes Quality of Experience (QoE) becomes challenging. Each chunk should be prefetched at highest possible quality while rebufferings and quality switches are minimized. Scalable Video Coding (SVC), with its layered encoding of video, provides more flexibility for HAS clients. It can reduce the occurrence of rebufferings under variable network conditions. However, SVC introduces at least 10% overhead on video bitrate per layer and increases the number of HTTP requests to fetch video chunks. So streaming SVC video at high qualities is more expensive. We propose a solutions that employs both SVC and non-SVC video to improve user's QoE while avoiding the increased bandwidth overhead and HTTP signaling of SVC. Our experiments using real-world bandwidth traces show that this method improves QoE compared to the state-of-the-art adaptation methods under various network conditions.

Cellular data sponsoring (CDS) is a traditional data sponsor scheme widely used in cellular video delivery networks, where content providers (CPs) bear the cellular data downloading cost for mobile video users (MUs), so as to attract more MUs and achieve higher revenue (e.g., via more attached advertisements). Edge caching sponsoring (ECS) is a novel data sponsor scheme recently introduced in the emerging 5G network, where CPs cache popular video contents on the edge network in advance and deliver them to local MUs directly. Thus, it can not only achieve the benefits of CDS (i.e., attracting more MUs and achieving higher revenue), but also reduce the congestion of backhaul network. In this work, we will perform a competitive analysis of CDS and ECS for mobile video streaming. Specifically, we consider a mobile video delivery network with two CPs who adopt CDS and ECS, respectively. MUs can choose one or neither of these two sponsor schemes (from the corresponding CPs) for his video content requests. We formulate the interaction of CPs and MUs as a two-stage Stackelberg game, where CPs act as leaders determining the efforts of their adopted sponsor schemes in the first stage, and MUs act as followers choosing the best sponsor schemes for their content requests in the second stage. We analyze the sub-game perfect equilibrium systematically for both cooperative and competitive scenarios (depending on whether two CPs cooperate or compete with each other). Numerical results show that in the competitive scenario, the joint sponsor of ECS and CDS can increase the total MU payoff by 36% ~ 140%, comparing with that with only one sponsor scheme. Moreover, the CPs can benefit more from ECS than from CDS when the revenue is higher.

Nowadays Dynamic Adaptive Streaming over HTTP (DASH) is the most prevalent solution on the Internet for multimedia streaming and responsible for the majority of global traffic. DASH uses adaptive bit rate (ABR) algorithms, which select the video quality considering performance metrics such as throughput and playout buffer level. Pensieve is a system that allows to train ABR algorithms using reinforcement learning within a simulated network environment and is outperforming existing approaches in terms of achieved performance. In this paper, we demonstrate that the performance of the trained ABR algorithms depends on the implementation of the simulated environment used to train the neural network. We also show that the used congestion control algorithm impacts the algorithms' performance due to cross-layer effects.

Streaming over the wireless channel is challenging due to rapid fluctuations in available throughput. Encouraged by recent advances in cellular throughput prediction based on radio link metrics, we examine the impact on Quality of Experience (QoE) when using prediction within existing algorithms based on the DASH standard. By design, DASH algorithms estimate available throughput at the application level from chunk rates and then apply some averaging function. We investigate alternatives for modifying these algorithms, by providing the algorithms direct predictions in place of estimates or feeding predictions in place of measurement samples. In addition, we explore different prediction horizons going from one to three chunk durations. Furthermore, we induce different levels of error to ideal prediction values to analyse deterioration in user QoE as a function of average error.

We find that by applying accurate prediction to three algorithms, user QoE can improve up to 55% depending on the algorithm in use. Furthermore having longer horizon positively affects QoE metrics. Accurate predictions have the most significant impact on stall performance by completely eliminating them. Prediction also improves switching behaviour significantly and longer prediction horizons enable a client to promptly reduce quality and avoid stalls when the throughput drops for a relatively long time that can deplete the buffer. For all algorithms, a 3-chunk horizon strikes the best balance between different QoE metrics and, as a result, achieving highest user QoE. While error-induced predictions significantly lower user QoE in certain situations, on average, they provide 15% improvement over DASH algorithms without any prediction.

We propose and evaluate simple signals coming from in-network telemetry that are effective to enhance the quality of DASH streaming. Specifically, in-network caching is known to positively affect DASH streaming quality but at the same time negatively affect the controller stability, increasing the quality switch ratio. Our contributions are to first (i) consider the broad spectrum of interaction between the network and the application, and then (ii) to devise how to effectively exploit in a DASH controller a very simple signal (i.e., per-quality hit ratio) that can be exported by framework such as Server and Network Assisted DASH (SAND) at fairly low rate (i.e., a timescale of 10s of seconds). Our thorough experimental campaign confirms the soundness of the approach (that significantly ameliorate performance with respect to network-blind DASH), as well as its robustness (i.e., tuning is not critical) and practical appeal (i.e., due to its simplicity and compatibility with SAND).

1 360-degree video streaming for virtual reality (VR) is emerging. However, the computing power and bandwidth of the current VR are limited when compared to the high-quality VR. To overcome these limits, this study proposes 360 video tiled streaming method that transmits 360-degree videos using the high efficiency video coding (HEVC) and the scalability extension of HEVC (SHVC). The proposed SHVC and HEVC encoders generate the bitstream that can transmit tiles independently. The proposed extractor extracts the bitstream of the tiles corresponding to the viewport. SHVC video bitstream extracted by the proposed methods consist of (i) an SHVC base layer (BL) which represents the entire 360-degree area and (ii) an SHVC enhancement layer (EL) for selective streaming with viewport (region of interest (ROI)) tiles. When the proposed HEVC encoder is used, low and high resolution sequences are separately encoded as the BL and EL of SHVC. By streaming the BL (low resolution) and selective EL (high resolution) tiles with ROI instead of streaming whole high quality 360-degree video, the proposed method can reduce the network bandwidth as well as the computational complexity on the decoder side. Experimental results show more than 47% bandwidth reduction.

The rise of video streaming has placed significant demands on network infrastructure. These demands are most acutely felt in the wireless space where limited resources are available. Compounding the matter, most techniques for adapting to network dynamics have been developed with wired networks in mind thus making performance in congested wireless networks, especially WiFi, quite problematic. In this paper, we propose a novel cross-layer design to improve video bitrate selection by incorporating MAC layer information. We design a lightweight channel characterization method that can provide an accurate airtime estimation based on the observation of WiFi control packets. We then devise a bitrate adaptation algorithm that can judiciously avoid faulty bitrate increases whenever severe channel competition is detected. Through extensive lab experiments, we show that our proposed method can significantly reduce video stall rates by up to 30x (from 65% to 2%) compared to existing methods.