Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB) to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss.
The motivation for this paper is to provide high-quality multimedia service over mobile networks. In a mobile network, two trends make it difficult to improve multimedia service. First, the introduction of smart phones has dramatically increased the volume of video traffic over mobile networks [
In this paper, we present a solution for enhancing the quality of streaming services over mobile networks. One solution is to improve the capacities of wired and wireless links between the multimedia streaming server and the mobile client. However, updating the mobile network infrastructure is too expensive. Even though Internet Service Providers (ISPs) have continued to improve the speed of mobile networks, they cannot satisfy user thirst for high-quality multimedia services.
Another solution is to decrease the error rate of mobile networks. Streaming services over mobile networks deliver media data under error-prone network environments [
To address this problem, content providers (CPs) calibrate streaming and cache servers through a scalable streaming scheme depending on network status. If the CP provides media at different qualities without a scalable streaming scheme, they would need to store all these different media on their own servers and incur costs associated with maintenance of redundant media of different quality. This would increase the cost of maintaining media streams [
Figure
Diagram of scalable streaming in a wireless environment.
Regardless of the benefit of a scalable streaming service, the relationship between layers degrades the quality of the scalable streaming service over an error-prone network. Even though one layer is transmitted successfully, the absence of a reference layer wastes other related layers at the decoder. Therefore, the loss of one packet invalidates its own layer and its referring layer. To improve the performance of scalable streaming services over error-prone networks, we should reduce the dependency between layers, thereby decreasing the amount of related media data for one packet and wasted media data caused by single-packet loss. We suggest an
In summary, in this paper we provide the following contributions. We present a service design for an
There are two schemes for adapting the quality of multimedia stream services based on network status:
In an adaptive streaming scheme, redundant multimedia streams with different quality data are stored in a storage area. Based on the bandwidth, adaptive streaming schemes can switch which stream to send to the user. Figure
Adaptive streaming scheme.
Figure
Scalable streaming scheme.
One media file has various frames, each of which shows one scene in the stream. There are three kinds of frames in the stream: the I frame, the P frame, and the B frame. The I frame contains all the information for showing one scene, whereas a decoder needs to be used to get additional information from other frames for decoding P or B frames. The P frame requires some information from the previous P or I frame, whereas the B frame needs to obtain information from the previous P or I frame and the future P or I frame at the same time.
The hierarchical structure between frames is critical to determining the quality of the scalable streaming service. The relationship between layers determines which layer can be available at the decoder. The scalable stream extracts multiple layers from one stream following each policy. The referring layer cannot be decoded without the reference layer. Therefore, the scalable stream increases the dependency between layers, adding an interframe relationship, thereby complicating the relationship between layers and making them harder to decode.
Figure
MP4 scalable streaming scheme.
In Figure
H.264 scalable streaming scheme.
In this paper, we propose an
Numerous schemes have been proposed for handling partial errors in packets. To enhance the quality of a scalable streaming service, [
Also, there has been much research on transcoding schemes. ISP proxies, a task dispatcher, and a client provide the transcoding scheme through multiple caching policies in [
For improving streaming service quality, active intermediate nodes have been deployed during streaming [
In [
A set-top box is an intermediate node located between the wired network and the wireless network through the streaming service. In [
The quality of a scalable streaming service is influenced by the dependency among the scalable layers in a scalable streaming service. The hierarchical relationship between scalable layers determines the decoding possibilities for transmitting the packet to the client over the wireless network. When reference frames are not transmitted successfully, the referring frames cannot be decoded. The complicated reference relationships between scalable layers of H.264 streaming increases the possibility of discarding the referring frame. Figures
Our
When whole packets are available, the layer can be decoded in the scalable stream. When one packet is lost, other data in the layer cannot be used for the decoding. Therefore, validation of the layer can be assured only when all its packets are available. Based on the terms in Table
Scalable streaming conversion variables.
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RF |
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NG | Number of GoVs in the multimedia stream |
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Number of frames at the |
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Number of reference frames of the |
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Number of layers of the |
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Number of packets in the |
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PLR | Packet loss rate |
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Validity of the |
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Validity of the |
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Validity of the |
When the error rate of the wireless link increases, most discarded scalable layers do not satisfy this equation. One frame is divided into several layers, so the reference layer is required to decode the referring scalable layer in the scalable streaming service. The number of scalable layers available is based on the vertical dependency among scalable layers. The validity of the scalable layer is given by
Finally, the decoder should check whether reference frames are available. The decoder does not require all the scalable layers of the reference frame to decode the referring frame. If the first layer of the reference frame is available, the reference frame can be decoded, and the validity of the scalable layer in the stream is given by
When the error rate of the wireless link decreases, most of the discarded scalable layers will not satisfy this equation.
For verifying the performance of our
We used the Joint Scalable Video Model (JSVM) [
Scalable streams.
Layer |
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Frame rate | Frame size |
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0 | 38.0 | 15 | 320 |
1 | 32.0 | 30 | 320 |
2 | 30.0 | 30 | 320 |
3 | 28.0 | 30 | 640 |
4 | 26.0 | 30 | 640 |
Layer 0 is encoded at 15 frames per seconds (fps) with a QP of 38. In addition, the resolution of the base layer is suitable for a
Scalable streams.
Frame name | Genre | Number of frames |
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Amazing Caves | Adventure | 2031 |
The Bourne Ultimatum | Action | 2125 |
I Am legend | Drama | 2397 |
Fantastic 4 | Action | 3017 |
Foreman | Video Clip | 399 |
To the Limit | Adventure | 919 |
Figure
Simulation environment.
In Figure
MPEG standards recommend that the decoder skip corrupted multimedia data in the next synchronization position (e.g.,
For detecting corrupted multimedia data in the simulation, the stream server adds more information to the generating packets, including frame_no, frame_seq, layer_id, and frame_flag. Here, frame_no stands for the order of the transmitted frame, and frame_seq is the sequence number of the packets. Our
Figure
Simulation packet management.
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Ratio between indirect lost layers and received layers.
In
Figures
Ratio between decoded layers and sent layers (based on bytes).
Ratio between decoded layers and sent layers (based on frames).
In Figure
In
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In the
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Our simulation results show that a scalable scheme with low dependence among scalable layers provides good service to users. At low wireless network error rates, the relationship among scalable layers determines the quality of scalable streams; this is especially critical for streams with small sized frames.
In this paper, we show that our
We perform packet-level analysis for scalable streaming service over a wireless network. Additionally, we suggest formulas for the expected quality of the scalable streaming service and prove the performance of our
The authors declare that there is no conflict of interests regarding the publication of this paper.
This paper is a revised and extended version of a paper that was originally presented at the 2014 FTRA International Symposium on Frontier and Innovation in Future Computing and Communications. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Education (2013R1A1A2063006).