The Common Media Application Format (CMAF) is a standard for adaptive bitrate live streaming. The CMAF adapts chunk encoding and enables low-latency live streaming. However, conventional bandwidth estimation for adaptive bitrate streaming underestimates bandwidth because download time is affected not only by network bandwidth but also by the idle times between chunks in the same segment. Inaccurate bandwidth estimation decreases the quality of experience of the streaming client. In this paper, we propose a chunk-grouping method to estimate the available bandwidth for adaptive bitrate live streaming. In the proposed method, by delaying HTTP request transmission and bandwidth estimation using grouped chunks, the client estimates the available bandwidth accurately due to there being no idle times in the grouped chunks. In addition, we extend the proposed method to dynamically change the number of grouping chunks according to buffer length during downloading of the previous segment. We evaluate the proposed methods under various network conditions in order to confirm the effectiveness of the proposed methods.

Peer-to-peer (P2P) technology has gained popularity as a way to enhance system performance. Nodes in a P2P network work together by providing network resources to one another. In this study, we examine the use of P2P technology for video streaming and develop a distributed incentive mechanism to prevent free-riding. Our proposed solution combines WebTorrent and the Solana blockchain and can be accessed through a web browser. To incentivize uploads, some of the received video chunks are encrypted using AES. Smart contracts on the blockchain are used for third-party verification of uploads and for managing access to the video content. Experimental results on a test network showed that our system can encrypt and decrypt chunks in about 1/40th the time it takes using WebRTC, without affecting the quality of video streaming. Smart contracts were also found to quickly verify uploads in about 860 milliseconds. The paper also explores how to effectively reward virtual points for uploads.

This paper presents a novel cooperative recording scheme in networked PVRs based on P2P networks to increase storage efficiency compared with when PVRs operate independently of each other, while maintaining program availability to a similar degree. We employ an erasure coding technique to guarantee data availability of recorded programs in P2P networks. We determine the data redundancy degree of recorded programs so that the system can support all the concurrent streaming requests for them and maintain as much availability as needed. We also present how to assign recording tasks to PVRs and playback the recorded programs without performance degradation. We show that our proposed scheme improves the storage efficiency significantly, compared with when PVRs do not cooperate with each other, while keeping the playbackability of each request similarly.

In this study, we propose a mechanism called adaptive failsoft control to address peak traffic in mobile live streaming, using a chasing playback function. Although a cache system is avaliable to support the chasing playback function for live streaming in a base station and device-to-device communication, the request concentration by highlight scenes influences the traffic load owing to data unavailability. To avoid data unavailability, we adapted two live streaming features: (1) streaming data while switching the video quality, and (2) time variability of the number of requests. The second feature enables a fallback mechanism for the cache system by prioritizing cache eviction and terminating the transfer of cache-missed requests. This paper discusses the simulation results of the proposed mechanism, which adopts a request model appropriate for (a) avoiding peak traffic and (b) maintaining continuity of service.

In this paper, we propose a method to enhance the download efficiency of BitTorrent protocol with the notion of structures in the set of pieces generated from a shared file and the swarm of peers downloading the same shared file. More specifically, as for the set of pieces, we introduce the notion of super-pieces called clusters, which is aimed to enlarge the granularity of the management of request-and-reply of pieces, and as for the swarm of peers, we organize a clique consisting of several peers with similar upload capacity, to improve the smoothness of the flow of pieces associated with a cluster. As is shown in the simulation results, the proposed extensions significantly reduce the download time of the first 75% of the downloaders, and thereby improve the performance of P2P-assisted video streaming such as Akamai NetSession and BitTorrent DNA.

Recently, information centric network (ICN) has attracted attention because cached content delivery from router's cache storage improves quality of service (QoS) by reducing redundant traffic. Then, adaptive video streaming is applied to ICN to improve client's quality of experience (QoE). However, in the previous approaches for the cache control, the router implicitly caches the content requested by a user for the other users who may request the same content subsequently. As a result, these approaches are not able to use the cache effectively to improve client's QoE because the cached contents are not always requested by the other users. In addition, since the previous cache control does not consider network congestion state, the adaptive bitrate (ABR) algorithm works incorrectly and causes congestion, and then QoE degrades due to unnecessary congestion. In this paper, we propose an explicit cache placement notification for congestion-aware adaptive video streaming over ICN (CASwECPN) to mitigate congestion. CASwECPN encourages explicit feedback according to the congestion detection in the router on the communication path. While congestion is detected, the router caches the requested content to its cache storage and explicitly notifies the client that the requested content is cached (explicit cache placement and notification) to mitigate congestion quickly. Then the client retrieve the explicitly cached content in the router detecting congestion according to the general procedures of ICN. The simulation experiments show that CASwECPN improves both QoS and client's QoE in adaptive video streaming that adjusts the bitrate adaptively every video segment download. As a result, CASwECPN effectively uses router's cache storage as compared to the conventional cache control policies.

Since the quality of video streaming services is degraded due to the encoding, network congestion, and lack of a playout buffer, the normality of services needs to be monitored by gathering the quality measured at the end clients. When measuring quality at the end clients, the computational power should be sufficiently low, the bitstream information cannot be accessed for the quality estimation due to the encryption, and reference video cannot be used at the end clients. Therefore, metadata-based models have been developed and standardized that take metadata such as the resolution, framerate, and bitrate, and stalling information as input and calculate the quality. However, calculated quality for linear TV and video on demand (VoD) services cannot be compared because metadata-based models cannot calculate the impacts of codec strategies (e.g., H.264/AVC, H.265/HEVC, and AV1) and configurations (e.g., 1-pass encoding for linear TV or 2-pass encoding for VoD) on the quality. To take into account the impact of codec strategies and configurations, coefficients of metadata-based model need to be optimized per codec strategy and configuration using subjective quality. However, extensive subjective assessment tests are difficult to frequently conduct because they are expensive and time consuming and need to be conducted by video quality experts. Therefore, to monitor the quality of several types of video streaming services (e.g., linear TV and VoD) and to compare these qualities, a derivation procedure is proposed for obtaining coefficients of metadata-based models using a full-reference model. To validate the procedure, extensive subjective assessment tests were conducted. Finally, it is shown that three metadata-based models (i.e., the P.1203.1 mode 0 model, extended P.1203.1 mode 0 model, and model proposed by Yamagishi et al.) based on the proposed procedure using the video multimethod assessment fusion (VMAF) algorithm estimate quality accurately in terms of root mean squared error.

With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user's viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was difficult because the QoE study is still in its infancy. At present, after tremendous devoted efforts have contributed to this research area, a shape of the QoE management architecture has become clear. Moreover, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and finally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the field of QoE studies.

For 360-degree video streaming, a 360-degree video is divided into segments temporally (i.e. some seconds). Each segment consists of multiple video tiles spatially. In this paper, we propose a tile quality selection method for tile-based video streaming. The proposed method suppresses the spatial quality variation within the viewport caused by a change of the viewport region due to user head movement. In the proposed method, the client checks whether the difference in quality level between the viewport and the region around the viewport is large, and if so, reduces it when assigning quality levels. Simulation results indicate that when the segment length is long, quality variation can be suppressed without significantly reducing the perceived video quality (in terms of bitrate). In particular, the quality variation within the viewport can be greatly suppressed. Furthermore, we verify that the proposed method is effective in reducing quality variation within the viewport and across segments without changing the total download size.

Online learning is a method which updates the model gradually and can modify and strengthen the previous model, so that the updated model can adapt to the new data without having to relearn all the data. However, the accuracy of the current online multiclass learning algorithm still has room for improvement, and the ability to produce sparse models is often not strong. In this paper, we propose a new Multiclass Truncated Gradient Confidence-Weighted online learning algorithm (MTGCW), which combine the Truncated Gradient algorithm and the Confidence-weighted algorithm to achieve higher learning performance. The experimental results demonstrate that the accuracy of MTGCW algorithm is always better than the original CW algorithm and other baseline methods. Based on these results, we applied our algorithm for phishing website recognition and image classification, and unexpectedly obtained encouraging experimental results. Thus, we have reasons to believe that our classification algorithm is clever at handling unstructured data which can promote the cognitive ability of computers to a certain extent.

In video distribution services such as video streaming, the providers must satisfy the various quality demands of the users. One of the human-centric indexes used to assess video quality is the quality of experience (QoE). In video streaming, the video bitrate, video freezing time, and video bitrate switching are significant determiners of QoE. To provide high-quality video streaming services, adaptive streaming using the Moving Picture Experts Group dynamic adaptive streaming over Hypertext Transfer Protocol (MPEG-DASH) is widely utilized. One of the conventional bitrate selection methods for MPEG-DASH selects the bitrate such that the amount of buffered data in the playback buffer, i.e., the playback buffer level, can be maintained at a constant value. This method can avoid buffer overflow and video freezing based on feedback control; however, this method induces high-frequency video bitrate switching, which can degrade QoE. To overcome this issue, this paper proposes a bitrate selection method in an adaptive video steaming for MPEG-DASH to improve the QoE by minimizing the bitrate fluctuation. To this end, the proposed method does not change the bitrate if the playback buffer level is not around its upper or lower limit, corresponding to the full or empty state of the playback buffer, respectively. In particular, to avoid buffer overflow and video freezing, the proposed method selects the bitrate based on proportional-derivative (PD) control to maintain the playback buffer level at a target level, which corresponds to an upper or lower threshold of the playback buffer level. Simulations confirm that, the proposed method offers better QoE than the conventional method for users with various preferences.

Recently, adaptive streaming over information centric network (ICN) has attracted attention. In adaptive streaming over ICN, the bitrate adaptation of the client often overestimates a bitrate for available bandwidth due to congestion because the client implicitly estimates congestion status from the content download procedures of ICN. As a result, streaming overestimated bitrate results in QoE degradation of clients such as cause of a stall time and frequent variation of the bitrate. In this paper, we propose a congestion-aware adaptive streaming over ICN combined with the explicit congestion notification (CAAS with ECN) to avoid QoE degradation. CAAS with ECN encourages explicit feedback of congestion detected in the router on the communication path, and introduces the upper band of the selectable bitrate (bitrate-cap) based on explicit feedback from the router to the bitrate adaptation of the clients. We evaluate the effectiveness of CAAS with ECN for client's QoE degradation due to congestion and behavior on the QoS metrics based on throughput. The simulation experiments show that the bitrate adjustment for all the clients improves QoE degradation and QoE fairness due to effective congestion avoidance.

Mobile video traffic is expected to increase explosively because of the proliferating number of Wi-Fi terminals. An overloaded multiple-input multiple-output (MIMO) technique allows the receiver to implement smaller number of antennas than the transmitter in exchange for degradation in video quality and a large amount of computational complexity for postcoding at the receiver side. This paper proposes a novel linear precoder for high-quality video streaming in overloaded multiuser MIMO systems, which protects visually significant portions of a video stream. A low complexity postcoder is also proposed, which detects some of data symbols by linear detection and the others by a prevoting vector cancellation (PVC) approach. It is shown from simulation results that the combination use of the proposed precoder and postcoder achieves higher-quality video streaming to multiple users in a wider range of signal-to-noise ratio (SNR) than a conventional unequal error protection scheme. The proposed precoder attains 40dB in peak signal-to-noise ratio even in poor channel conditions such as the SNR of 12dB. In addition, due to the stepwise acquisition of data symbols by means of linear detection and PVC, the proposed postcoder reduces the number of complex additions by 76% and that of multiplications by 64% compared to the conventional PVC.

In this paper the quality of adaptive bit rate video streaming is investigated and two state-of-the-art models, i.e., the NTT audiovisual quality-estimation and ITU-T P.1203 models, are considered. This paper shows how these models can be applied to new conditions, e.g., 4K ultra high definition (4K-UHD) videos encoded using H.265, considering that they were originally designed and trained for HD videos encoded with H.264. Six subjective evaluations involving up to 192 participants and a large variety of test conditions, e.g., durations from 10sec to 3min, coding-quality variation, and stalling events, were conducted on both TV and mobile devices. Using the subjective data, this paper addresses how models and coefficients can be transferred to new conditions. A comparison between state-of-the-art models is conducted, showing the performance of transferred and retrained models. It is found that other video-quality estimation models, such as VMAF, can be used as input of the NTT and ITU-T P.1203 long-term pooling modules, allowing these other video-quality-estimation models to support the specificities of adaptive bit-rate-streaming scenarios. Finally, all retrained coefficients are detailed in this paper allowing future work to directly reuse the results of this study.

In conventional video streaming systems, various kind of video streams are delivered from a dedicated server (e.g., edge server) to the subscribers so that a video stream of higher quality level is encoded with a higher bitrate. In this paper, we consider the problem of delivering those video streams with the assistance of Peer-to-Peer (P2P) technology with as small server cost as possible while keeping the performance of video streaming in terms of the throughput and the latency. The basic idea of the proposed method is to divide a given video stream into several sub-streams called stripes as evenly as possible and to deliver those stripes to the subscribers through different tree-structured overlays. Such a stripe-based approach could average the load of peers, and could effectively resolve the overloading of the overlay for high quality video streams. The performance of the proposed method is evaluated numerically. The result of evaluations indicates that the proposed method significantly reduces the server cost necessary to guarantee a designated delivery hops, compared with a naive tree-based scheme.

This paper considers Peer-to-Peer (P2P) video streaming systems, in which a given video stream is divided into b stripes and those stripes are delivered to n peers through b spanning trees under the constraint such that each peer including the source can forward at most b stripes. The delivery of a stripe to n peers is said to be a k-hop delivery if all peers receive the stripe through a path of length at most k. Let Bk=∑i=0k-1bi. It is known that under the above constraint, k-hop delivery of b stripes to n peers is possible only if n≤Bk. This paper proves that (k+1)-hop delivery of b stripes to n peers is possible for any n≤Bk; namely, we can realize the delivery of stripes with a guaranteed latency while it is slightly larger than the minimum latency. In addition, we derive a necessary and sufficient condition on n to enable a k-hop delivery of b stripes for Bk-b+2≤n≤Bk-1; namely for n's close to Bk.

The fifth-generation (5G) mobile communication system is being researched and developed for launch as a commercial service in 2020. The 5G mobile network will include many radio access technologies, such as LTE, 5G NR, and WLAN. Therefore, a user equipment (UE) will be connected to different types of base stations as it moves within a 5G heterogeneous network. Accordingly, it is assumed that the throughput will change with each change in the serving cell. The 5G mobile network is expected to serve large capacity contents, such as 4K videos. However, a conventional video streaming method cannot effectively use the available bandwidth in a 5G heterogeneous network. In this study, we propose a sending rate adaptation method based on predictions for the available bandwidth. In the proposed method, the available bandwidth is predicted from the communication log data. The communication logging database, including past throughput with its location, is created by a UE. A UE refers to the communication log data for predictions when the serving cell is likely to change. We develop a video streaming device that implements the proposed method and evaluates its performance. The results show that the proposed method can change the sending rate and resolution according to the available bandwidth. The proposed method increases the probability of transmitting high-resolution video, which is not possible with conventional methods. Moreover, we performed subjective evaluation of the transmitted video by the proof-of-concept test. The result of the subjective evaluation shows that the proposed method improves the quality of experience for video streaming.

This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.

360-degree video delivery in Virtual Reality is very challenging due to the fact that 360-degree videos require much higher bandwidth than conventional videos. To overcome this problem, viewport-adaptive streaming has been introduced. In this study, we propose a new adaptation method for tiling-based viewport-adaptive streaming of 360-degree videos. For content preparation, the Cubemap projection format is used, where faces or parts of a face are encoded as tiles. Also, the problem is formulated as an optimization problem, in which each visible tile is weighted based on how that tile overlaps with the viewport. To solve the problem, an approximation algorithm is proposed in this study. An evaluation of the proposed method and reference methods is carried out under different tiling schemes and bandwidths. Experiments show that the Cubemap format with tiling provides a lot of benefits in terms of storage, viewport quality across different viewing directions and bandwidths, and tolerance to prediction errors.

In this paper, we propose a quality-level selection method for adaptive video streaming with scalable video coding (SVC). The proposed method works on the client with the dynamic adaptive streaming over HTTP (DASH) with SVC. The proposed method consists of two components: introducing segment group and a buffer-aware layer selection algorithm. In general, quality of experience (QoE) performance degrades due to stalling (playback buffer underflow), low playback quality, frequent quality-level switching, and extreme-down quality switching. The proposed algorithm focuses on reducing the frequent quality-level switching, and extreme-down quality switching without increasing stalling and degrading playback quality. In the proposed method, a SVC-DASH client selects a layer every G segments, called a segment group to prevent frequent quality-level switching. In addition, the proposed method selects the quality of a layer based on a playback buffer in a layer selection algorithm for preventing extreme-down switching. We implement the proposed method on a real SVC-DASH system and evaluate its performance by subjective evaluations of multiple users. As a result, we confirm that the proposed algorithm can obtain better mean opinion score (MOS) value than a conventional SVC-DASH, and confirm that the proposed algorithm is effective to improve QoE performance in SVC-DASH.