Video quality estimation system and video quality estimation method

Abstract

To grasp the relation between the resolution and bit rate of a moving image without costing enormously.SOLUTION: A moving image quality estimation system comprises: an estimation part which estimates a first bit rate corresponding to the resolution of a moving image based upon network quality information on a network associated with distribution of the moving image and moving image quality information including the resolution of the moving image and a second bit rate corresponding to the resolution of the moving image; and a calculation part which calculates a value to be added to the second bit rate, and the estimation part adds the value to the second bit rate to estimate a first bit rate.SELECTED DRAWING: Figure 21

Description

[Technical Field]

[0001] The present disclosure relates to a video quality estimation device, a video quality estimation method, and a video quality estimation system. [Background technology]

[0002] In recent years, the demand for video distribution services has been increasing. However, video traffic consumes a lot of bandwidth, making reducing video traffic a major challenge in network operation.

[0003] For this reason, technologies for reducing video traffic have recently been proposed. For example, Patent Document 1 discloses a technology that estimates the throughput when downloading a video and selects a bit rate that minimizes the traffic volume based on the estimated throughput.

[0004] Another technique for reducing video traffic is traffic shaping, a bandwidth control technique that limits the bit rate of video data to a fixed bit rate (shaping rate). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2019-016961 Summary of the Invention [Problem to be solved by the invention]

[0006] Incidentally, when performing traffic shaping, network operators have a demand to know at what bit rate shaping is performed and at what resolution video is being played on the terminal. In other words, network operators have a demand to know the relationship between video resolution and bit rate.

[0007] However, the resolution of the video being played on a device fluctuates due to fluctuations in network quality. For this reason, to check the video resolution, the network operator must actually watch the video on the device. Therefore, in order for the network operator to understand the relationship between video resolution and bitrate, it is necessary to collect a huge amount of data, which is a huge cost.

[0008] Therefore, an object of the present disclosure is to provide a video quality estimation device, a video quality estimation method, and a video quality estimation system that can solve the above-mentioned problems and grasp the relationship between video resolution and bit rate without incurring huge costs. [Means for solving the problem]

[0009] An apparatus for estimating moving image quality according to one aspect includes: a first collection unit that collects network quality information of a network related to video distribution; a second collection unit that collects video quality information of the video; an estimation unit that estimates a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; Equipped with.

[0010] A video quality estimation method according to one aspect includes: A first collection step of collecting network quality information of a network related to video distribution; a second collecting step of collecting video quality information of the video; an estimation step of estimating a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; Includes:

[0011] A video quality estimation system according to one aspect includes: a first collection unit that collects network quality information of a network related to video distribution; a second collection unit that collects video quality information of the video; an estimation unit that estimates a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; Equipped with. [Effects of the Invention]

[0012] According to the above-described aspects, it is possible to provide a moving image quality estimation device, a moving image quality estimation method, and a moving image quality estimation system that can grasp the relationship between the resolution and bit rate of a moving image without incurring huge costs. [Brief explanation of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram illustrating an example of an ABR streaming method. [Figure 2] FIG. 10 is a diagram illustrating an example of traffic shaping. [Figure 3] FIG. 10 is a diagram illustrating an example of traffic shaping. [Figure 4] FIG. 10 is a diagram illustrating an example of video quality information. [Figure 5] FIG. 10 is a diagram showing an example of a resolution distribution when shaping is actually performed. [Figure 6] FIG. 10 is a diagram illustrating an example of a resolution distribution estimated from video quality information. [Figure 7] FIG. 1 is a diagram illustrating an example of an outline of the operation of a video quality estimation device according to each embodiment. [Figure 8] FIG. 10 is a diagram showing an example of a mathematical formula used when estimating a bit rate corresponding to a video resolution in the video quality estimation device according to each embodiment. [Figure 9] 1 is a block diagram showing an example of the configuration of a moving image quality estimation device according to a first embodiment. [Figure 10] FIG. 10 is a diagram showing an example of the resolution of a video being played on a terminal. [Figure 11] 10A and 10B are diagrams illustrating examples of video resolutions requested from a terminal to a video distribution server. [Figure 12]FIG. 10 is a diagram illustrating an example of valid areas and invalid areas of a resolution distribution. [Figure 13] 1 is a diagram illustrating an example of a network arrangement of a moving image quality estimation device according to a first embodiment. [Figure 14] 1 is a diagram illustrating an example of a network arrangement of a moving image quality estimation device according to a first embodiment. [Figure 15] 4 is a flowchart showing an example of the flow of operations of the moving image quality estimation device according to the first embodiment. [Figure 16] FIG. 3 is a diagram showing an example of verifying the effects of the moving image quality estimation device according to the first embodiment. [Figure 17] FIG. 10 is a block diagram showing an example of the configuration of a moving image quality estimation device according to a second embodiment. [Figure 18] FIG. 10 is a diagram illustrating an example of an outline of the operation of the moving image quality estimation device according to the second embodiment. [Figure 19] 10 is a flowchart showing an example of the flow of operations of the moving image quality estimation device according to the second embodiment. FIG. [Figure 20] FIG. 10 is a diagram illustrating an example of an outline of the operation of a modified example of the moving image quality estimation device according to the second embodiment. [Figure 21] 1 is a block diagram conceptually illustrating an example of the configuration of a moving image quality estimation device according to an embodiment. [Figure 22] 22 is a flowchart showing an example of the flow of operations of the moving image quality estimation device shown in FIG. 21. FIG. [Figure 23] 22 is a diagram illustrating an example of the configuration of a moving image quality estimation system including the moving image quality estimation device shown in FIG. 21. FIG. [Figure 24] FIG. 2 is a block diagram showing an example of the hardware configuration of a computer that realizes the video quality estimation device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] Before describing the embodiments of the present disclosure, the details of the problem to be solved by the present disclosure and an outline of the operation of each embodiment of the present disclosure will be described in detail.

[0015] <Problem of the present disclosure> First, the problem to be solved by the present disclosure will be described in detail. The currently mainstream video distribution method is the ABR (Adaptive Bit Rate) streaming method, typified by ABR streaming over HTTP (Hypertext Transfer Protocol).

[0016] The ABR streaming method is standardized by MPEG-DASH (Moving Picture Experts Group - Dynamic Adaptive Streaming over HTTP) and aims to deliver video at the highest quality possible without exceeding the available network bandwidth.

[0017] More specifically, in the ABR streaming method, as shown in Fig. 1, terminal 10 requests video of the maximum quality (here, quality is resolution) that does not exceed the available bandwidth of the network from video distribution server 80. Video distribution server 80 stores video of various qualities, and transmits video of the quality requested by terminal 10 to terminal 10. At this time, the video is transmitted in units called chunks.

[0018] As mentioned above, in the ABR streaming method, the resolution is adjusted to provide stable video quality within the available network bandwidth. Therefore, the video quality can be controlled by traffic shaping.

[0019] As shown in Figures 2 and 3, traffic shaping is a bandwidth control technology that limits the bit rate of video data to a fixed bit rate (shaping rate) and controls the resolution of the video data. Traffic shaping can be performed at any point on the network.

[0020] Here, when performing traffic shaping, network operators have a demand to know at what bit rate shaping is performed and at what resolution video is being played back on terminal 10. In other words, network operators have a demand to know the relationship between video resolution and bit rate.

[0021] If a network operator could grasp the relationship between video resolution and bit rate, it would be possible to determine, for example, a shaping rate index for providing a certain video at a certain resolution, and to provide the certain video at a higher resolution according to the shaping rate. Furthermore, if the network operator could provide a certain video at a high resolution, it would be possible to notify users of terminal 10 that "video viewing at high resolution is possible on this network!"

[0022] However, the resolution of the video being played on the terminal 10 varies due to the influence of fluctuations in network quality. For this reason, in order to check the resolution of the video, the network operator must actually watch the video. Therefore, in order for the network operator to understand the relationship between the video resolution and the bit rate, it is necessary to collect a huge amount of data, which is a problem in that it requires a huge amount of cost. Each embodiment of the present disclosure described below contributes to solving the above-mentioned problems.

[0023] <Overview of Operation of the Embodiment of the Present Disclosure> Next, an outline of the operation of each embodiment of the present disclosure will be described. The bit rate corresponding to the video resolution can sometimes be obtained without actually watching the video.

[0024] For example, video quality information such as "get_video_info" shown in FIG. 4 can be acquired for a certain video from the video sharing site YouTube (registered trademark).

[0025] According to the "get_video_info" shown in Figure 4, for a certain video, the resolution "1080P" and the corresponding average bit rate "661361" are listed as a pair, and the resolution "360P" and the corresponding average bit rate "2996197" are listed as a pair.

[0026] However, the resolution distribution estimated from the video quality information differs from the resolution distribution when the video is actually shaped. This point will be explained with reference to Figures 5 and 6. The resolution distribution is a distribution that represents the ratio of each resolution corresponding to the bit rate.

[0027] Fig. 5 shows an example of the resolution distribution when shaping 10 videos. In Fig. 5, the horizontal axis indicates the shaping rate, and the vertical axis indicates the ratio of each resolution. Specifically, Fig. 5 shows that when the shaping rate is set to 256 kbps, for example, approximately 90% of the videos are played at a resolution of 144P and approximately 10% are played at a resolution of 240P on terminal 10.

[0028] On the other hand, Fig. 6 shows an example of a resolution distribution estimated from video quality information of a video. In Fig. 6, the horizontal axis indicates the bit rate (shaping rate), and the vertical axis indicates the ratio similar to the vertical axis in Fig. 5.

[0029] Comparing Fig. 5 and Fig. 6, the resolution distribution estimated from the video quality information shown in Fig. 6 is significantly different from the resolution distribution at the time of actual shaping shown in Fig. 5. The reason for this is presumably that the actual resolution of the video being played on terminal 10 fluctuates due to the influence of fluctuations in network quality.

[0030] Therefore, in each embodiment of the present disclosure, as shown in FIG. 7, the bit rate corresponding to the video resolution is estimated by modifying the video quality information using network quality information (e.g., throughput, frame loss rate, etc.).

[0031] More specifically, in each embodiment of the present disclosure, as shown in FIG. 8, the bit rate [bps] required to transmit video of a certain resolution is estimated to be the bit rate corresponding to that resolution obtained from the video quality information plus the following bit rate: (1) Incremental bit rate R due to the behavior specific to the ABR streaming method ABR [bps] (2) Bit rate R for retransmission due to loss loss [bps] (3) Bit rate R for header and other overhead overhead [bps] The bit rates (1) to (3) above will be described in detail in the following embodiments of the present disclosure.

[0032] Hereinafter, details of each embodiment of the present disclosure will be described. Note that the following description and drawings have been omitted and simplified as appropriate for clarity of explanation. In addition, in each of the following drawings, the same elements are given the same reference numerals, and duplicate explanations will be omitted as necessary.

[0033] <First Embodiment> First, with reference to FIG. 9, an example of the configuration of the moving image quality estimation device 20 according to the first embodiment will be described.

[0034] As shown in FIG. 9, the moving image quality estimation device 20 according to the first embodiment includes a network quality information collection unit 21, a network quality information DB (Data Base) 22, a moving image quality information collection unit 23, a moving image quality information DB 24, and a moving image quality estimation unit 25.

[0035] The network quality information collecting unit 21 collects network quality information of a network related to video distribution. The network quality information includes a frame loss rate, an average throughput, etc. For example, the network quality information collecting unit 21 collects network quality information set in advance from a network operator. The network quality information DB 22 stores the network quality information collected by the network quality information collecting unit 21 .

[0036] When the terminal 10 to which the video is delivered is a mobile terminal, the network related to video distribution is a network consisting of a wireless network between the terminal 10 and a base station 30 (described later), a core network, the Internet 70 (described later), and a network on the video distribution server 80 side. The core network may be an MNO (Mobile Network Operator) network 40 (described later), or an MNO network 40 and an MVNO (Mobile Virtual Network Operator) network 50 (described later).

[0037] The video quality information collecting unit 23 collects video quality information for each of one or more videos. The video quality information includes the resolution, bit rate, etc. of the video. For example, the video quality information collecting unit 23 collects video quality information set in advance from the video distribution server 80 or a network operator. The moving image quality information DB 24 stores the moving image quality information collected by the moving image quality information collecting unit 23 .

[0038] The moving image quality estimation unit 25 estimates a bit rate corresponding to the resolution of the moving image based on the network quality information stored in the network quality information DB 22 and the moving image quality information stored in the moving image quality information DB 24. Furthermore, the moving image quality estimation unit 25 estimates a resolution distribution that indicates the ratio of each resolution corresponding to the bit rate of the moving image. Here, the video quality estimation unit 25 includes a policy influence calculation unit 251, a loss influence calculation unit 252, an overhead calculation unit 253, and a resolution distribution estimation unit 254.

[0039] The policy influence calculation unit 251 calculates the increment rate R due to the behavior specific to the ABR streaming method, as shown in FIG. ABR Calculate. The loss influence calculation unit 252 calculates the bit rate R of the retransmission due to the loss (2) shown in FIG. loss Calculate. The overhead calculation unit 253 calculates the bit rate R for the overhead of (3) the header etc. shown in FIG. overhead Calculate.

[0040] When estimating a bit rate corresponding to the resolution of the video to be estimated, the resolution distribution estimation unit 254 estimates the bit rate corresponding to the resolution of the video to be estimated by calculating the R ABR , R loss , and R overhead The resolution distribution estimation unit 254 then estimates the bit rate after this addition as the bit rate corresponding to the target resolution of the target moving image. The resolution distribution estimation unit 254 also performs this estimation for each resolution included in the video quality information of each moving image collected by the video quality information collection unit 23, thereby estimating a resolution distribution that indicates the ratio of each resolution to the bit rate.

[0041] The operations of the policy influence calculation unit 251, the loss influence calculation unit 252, the overhead calculation unit 253, and the resolution distribution estimation unit 254 will be described in detail below.

[0042] First, the operation of the policy influence calculation unit 251 will be described with reference to FIGS. As explained with reference to Figure 1, in the ABR streaming method, terminal 10 requests a video (chunk) of a certain resolution from video distribution server 80, and video distribution server 80 transmits the video of the resolution requested by terminal 10 to terminal 10.

[0043] Fig. 10 shows an example of the resolution of a video being played on terminal 10, and Fig. 11 shows an example of the resolution of a video being requested from terminal 10 to video distribution server 80. In Fig. 10 and Fig. 11, the horizontal axis represents time, and the vertical axis represents resolution.

[0044] As shown in FIGS. 10 and 11, the terminal 10 requests a video with a higher resolution of 240p while playing a video with a low resolution of 144p. From this, it is considered that the terminal 10 tends to try to increase the resolution when the resolution of the video being played back is low.

[0045] Therefore, when estimating a bit rate corresponding to the resolution of the video to be estimated, the policy influence calculation unit 251 calculates the increment rate R due to the behavior specific to the ABR streaming method according to whether the resolution of the video to be estimated is equal to or higher than the standard resolution, which is included in the video quality information of the video to be estimated. ABR is determined as follows: At this time, the standard resolution may be stored in advance in the network quality information DB 22, for example.

[0046] i) When the resolution is lower than standard resolution If the estimated resolution is lower than the standard resolution, the terminal 10 is likely to request a video with a higher resolution in an attempt to increase the resolution to the standard resolution. Therefore, the policy influence calculation unit 251 calculates R ABR Determine.

number

[0047] ii) When the resolution is standard resolution or higher If the resolution to be estimated is equal to or higher than the standard resolution, the terminal 10 is likely to continue requesting video at the current resolution since there is no need to increase the resolution. Therefore, the policy influence calculation unit 251 calculates R ABR Determine.

number

[0048] Next, the operation of the loss effect calculation unit 252 will be described. If a frame loss occurs, video data equivalent to the number of losses x 1 frame will be retransmitted. Therefore, when estimating a bit rate corresponding to the resolution of the video to be estimated, the loss effect calculation unit 252 calculates the bit rate R of the retransmission due to frame loss. loss is calculated as the expected bit rate of the retransmitted video data.

[0049] Bit rate β of retransmitted video data R (ρ) expectation value E[β R (ρ)] can be calculated using the frame loss rate ρ included in the network quality information as shown in the following formula 3.

number

[0050] Here, the probability that frame loss will not occur is 1-(probability that frame loss will not occur). Therefore, the probability that frame loss will not occur can be calculated using the following Equation 4.

number

[0051] The first term in the limit function of Equation 3 indicates the bit rate when no frame loss occurs, and the second term indicates the bit rate when n frame losses occur. Further calculation of Equation 3 results in Equation 5 below.

number

[0052] Here, the speed at which the value increases is faster when multiplied by n than when multiplied by n. Therefore, when the limit is taken as n → infinity, the nth power becomes dominant. Therefore, Equation 3 results in the above result.

[0053] Next, the operation of the overhead calculation unit 253 will be described. When transmitting video, video data with a header added is transmitted. Therefore, when estimating the video bitrate, the bitrate required for transmitting the header must also be taken into account as overhead. Furthermore, the bitrate required for transmitting the header varies depending on the header size.

[0054] Here, the video data is fragmented into pieces of MTU (Maximum Transmission Unit) size and transmitted. Therefore, when estimating a bit rate corresponding to the resolution of the video to be estimated, the overhead calculation unit 253 calculates the bit rate R for the overhead due to the header, where η is the expected value of the header size and β is the bit rate. overhead can be calculated by the following Equation 6. Note that β is the bit rate corresponding to the resolution of the video to be estimated, which is included in the video quality information of the video to be estimated.

number

[0055] Here, calculation of the expected value η of the header size requires each packet that constitutes the frame, so the expected value η of the header size is set to the maximum value taking into account the processing load.

[0056] For example, when the packets constituting the frame are TCP (Transmission Control Protocol) / HTTP packets, the expected value η of the header size is given by the following Equation 7.

number

[0057] Furthermore, when the packets constituting the frame are User Datagram Protocol (UDP) / Quick UDP Internet Connections (QUIC) / HTTP packets, the expected value η of the header size is given by the following Equation 8.

number

[0058] Next, the operation of the resolution distribution estimation unit 254 will be described. When estimating a bit rate corresponding to the resolution of the video to be estimated, the resolution distribution estimation unit 254 estimates the bit rate corresponding to the resolution of the video to be estimated by calculating the R ABR , R loss , and R overhead Then, the resolution distribution estimation unit 254 estimates the bit rate after this addition as the bit rate corresponding to the resolution of the moving image to be estimated.

[0059] Therefore, when the video to be estimated is played back at the resolution of the estimated video on terminal 10, the bit rate estimated to correspond to the resolution of the estimated video is used as the shaping rate to shape the video to be estimated.

[0060] However, when shaping is performed at a shaping rate that exceeds the average throughput of the network, the quality of the video played back on the terminal 10 remains unchanged compared to when shaping is performed at a shaping rate that is the same as the average throughput.

[0061] Therefore, the resolution distribution estimation unit 254 adjusts the bit rate estimated to correspond to the target resolution based on the average throughput of the network. Specifically, if the estimated bit rate is higher than the average throughput, the resolution distribution estimation unit 254 adjusts the estimated bit rate to the value of the average throughput, and otherwise leaves the estimated bit rate as is.

[0062] In this case, the distribution range of the shaping rate β is adjusted as shown in Fig. 12. In the example of Fig. 12, the shaping rate β is adjusted to the average throughput x ave Only the following areas are valid areas, and the shaping rate β is the average throughput x ave Any area higher than this is an invalid area.

[0063] As shown in Fig. 12, when the distribution range of the shaping rate β is adjusted, the resolution of the video played on the terminal 10 is adjusted as shown in the following Equation 9. That is, the resolution of the video played on the terminal 10 is adjusted when the shaping rate β is the average throughput x ave If it is greater than 1, the average throughput x ave In other cases, the resolution depends on the shaping rate β.

number

[0064] Next, a network layout example of the moving image quality estimation device 20 according to the first embodiment will be described with reference to Fig. 13 and Fig. 14. Note that in Fig. 13 and Fig. 14, the moving image distribution server 80 is not shown, but is provided at the end of the Internet 70 as viewed from the terminal 10. The moving image quality estimation device 20 according to the first embodiment is arranged inside a bandwidth control device 200 that controls the bandwidth of a moving image by, for example, shaping the moving image.

[0065] 13, the bandwidth control device 200 is arranged in an MNO network 40. The MNO network 40 is connected to a base station 30 and the Internet 70. In addition to the bandwidth control device 200, the MNO network 40 also includes an S-GW (Serving Gateway) 41, a P-GW (Packet Data Network Gateway) 42, an MME (Mobility Management Entity) 43, and an HSS (Home Subscriber Server) 44.

[0066] 14, the bandwidth control device 200 is arranged in an MVNO network 50. The MVNO network 50 is connected to an MNO network 40 via a network tunnel 60, and is also connected to the Internet 70. In addition to the bandwidth control device 200, the MVNO network 50 also includes a P-GW 51, a PCRF (Policy and Charging Rules Function) 52, and an authentication server 53. The MNO network 40 is also connected to a base station 30, and includes an S-GW 41.

[0067] Next, an example of the flow of operations of the moving image quality estimation device 20 according to the first embodiment will be described with reference to FIG. 15, first, the network quality information collecting unit 21 collects network quality information of a network related to video distribution (step S101). The collected network quality information is stored in the network quality information DB 22.

[0068] Next, the moving image quality information collecting unit 23 collects moving image quality information for each of the one or more moving images (step S102). The collected moving image quality information is stored in the moving image quality information DB 24. Note that steps S101 and S102 do not necessarily have to be performed in this order, but may be performed in the reverse order or simultaneously.

[0069] Next, the video quality estimation unit 25 selects one of one or more videos for which video quality information has been collected by the video quality information collection unit 23 as an estimation target, and also selects one of one or more resolutions included in the video quality information of the selected estimation target video as an estimation target (step S103).

[0070] Next, in the video quality estimation unit 25, for the resolution of the video to be estimated, based on the network quality information and the video quality information of the video to be estimated, the policy influence calculation unit 251 calculates (1) the increment rate R due to the behavior specific to the ABR streaming method. ABR (Step S104), and the loss influence calculation unit 252 calculates (2) the bit rate R loss (Step S105), and the overhead calculation unit 253 calculates (3) the bit rate R for the overhead of the header, etc. overhead is calculated (step S106). Note that steps S104 to S106 do not necessarily have to be performed in this order, but may be performed in any order or simultaneously.

[0071] Next, in the moving image quality estimation unit 25, the resolution distribution estimation unit 254 calculates the R ABR , R loss , and R overhead Then, the resolution distribution estimation unit 254 estimates the bit rate after the addition as the bit rate corresponding to the target resolution of the target video (step S107). At this time, the resolution distribution estimation unit 254 may adjust the estimated bit rate based on the average throughput of the network.

[0072] Next, the moving image quality estimation unit 25 determines whether or not there are any moving images and resolutions remaining to be selected as estimation targets in the moving image quality information collected by the moving image quality information collection unit 23 (step S108). For example, if a condition is set that all or a predetermined number of resolutions of all or a predetermined number of moving images included in the moving image quality information are to be estimation targets, the determination in step S108 is Yes if the condition has not yet been met.

[0073] In step S108, if there are still moving images and resolutions to be selected as estimation targets (Yes in step S108), the moving image quality estimation unit 25 returns to the processing of step S103, selects one moving image as the estimation target, and selects one resolution of the selected moving image as the estimation target, and then performs the processing of steps S104 to S107.

[0074] On the other hand, in step S108, if there are no remaining moving images and resolutions to be selected as estimation targets (No in step S108), the resolution distribution estimation unit 254 in the moving image quality estimation unit 25 estimates a resolution distribution representing the ratio of each resolution corresponding to the bit rate based on the estimation result of the bit rate estimated to correspond to the resolution of the moving image to be estimated (step S109).

[0075] As described above, according to the first embodiment, the network quality information collecting unit 21 collects network quality information of a network related to video distribution. The video quality information collecting unit 23 collects video quality information of the video. The video quality estimating unit 25 estimates a bit rate corresponding to the video resolution based on the network quality information and the video quality information.

[0076] In detail, when estimating a bit rate corresponding to the resolution of the video to be estimated, the video quality estimation unit 25 adds the bit rates (1) to (3) below to the bit rate corresponding to the resolution of the video to be estimated, which is included in the video quality information of the video to be estimated, and estimates the bit rate after the addition as the bit rate corresponding to the resolution of the video to be estimated. (1) Incremental bit rate R due to the behavior specific to the ABR streaming method ABR (2) Bit rate R for retransmission due to loss loss (3) Bit rate R for header and other overhead overhead

[0077] This allows network operators to determine the bit rate corresponding to a video resolution without having to actually watch the video and collect a huge amount of data, making it possible to understand the relationship between video resolution and bit rate without incurring huge costs.

[0078] Here, the effects of the first embodiment will be verified with reference to FIG. The bottom left diagram of Fig. 16 shows an example of resolution distribution when shaping 10 certain videos. The middle and bottom diagrams of Fig. 16 show an example of resolution distribution estimated from video quality information only. The bottom right diagram of Fig. 16 shows an example of resolution distribution estimated from video quality information, behavior specific to the ABR streaming method, retransmissions due to loss, and overhead such as headers in this first embodiment. The horizontal and vertical axes of the bottom left diagram of Fig. 16 are the same as those in Fig. 5, and the horizontal and vertical axes of the bottom middle and bottom right diagrams of Fig. 16 are the same as those in Fig. 6.

[0079] Here, in the lower middle and lower right diagrams of Figure 16, we determined whether the estimated resolution corresponding to each shaping rate was correct (whether it matches the resolution in the lower left diagram of Figure 16), and assigned a value of 1 if it was correct, or 0 if it was incorrect, and used the average value of each shaping rate as the identification accuracy.

[0080] As shown in the middle and bottom diagrams of Figure 16, the resolution distribution estimated from video quality information alone has a low classification accuracy of 31.7% and is a distribution that is significantly different from the resolution distribution in the bottom left diagram of Figure 16.

[0081] In contrast, as shown in the lower right diagram of Figure 16, in this embodiment 1, the resolution distribution estimated from video quality information, behavior specific to the ABR streaming method, retransmissions due to loss, and overhead such as headers has a high identification accuracy of 86.0% and is very close to the resolution distribution in the lower left diagram of Figure 16.

[0082] From this, it can be seen that according to this first embodiment, it is possible to estimate the relationship between video resolution and bit rate, taking into account fluctuations in network quality, i.e., the resolution distribution that represents the ratio of each resolution corresponding to the bit rate.

[0083] This makes it possible to estimate the resolution distribution when the network quality is certain. For example, it becomes possible to estimate the resolution distribution when the network quality is an average throughput of 3 Mbps and a frame loss rate of 0.1%.

[0084] Furthermore, if the network operator confirms by referring to the resolution distribution according to this embodiment 1 that videos can be provided at high resolution, the network operator will be able to notify users of terminal 10 that "videos can be viewed at high resolution on this network!"

[0085] Furthermore, network operators can use the resolution distribution according to the first embodiment as a guide to avoid excessive shaping. If the resolution distribution according to the first embodiment does not exist, a phenomenon occurs in which shaping is performed uniformly at a shaping rate of 300 [kbps]. On the other hand, if a resolution distribution according to the first embodiment exists, the resolution distribution enables the network operator to understand what the shaping rate is that can provide 90% or more of video at a resolution of 360p or higher.

[0086] <Embodiment 2> First, with reference to FIG. 17, an example of the configuration of a moving image quality estimation device 20A according to the second embodiment will be described.

[0087] As shown in Figure 17, the moving image quality estimation device 20A of this embodiment 2 differs from the configuration of the moving image quality estimation device 20 of Figure 9 of the above-mentioned embodiment 1 in that a display unit 26 is added. The display unit 26 displays the moving image quality, such as the resolution distribution, estimated by the moving image quality estimation unit 25 on the screen of the moving image quality estimation device 20A.

[0088] Furthermore, the moving image quality estimation device 20A according to the second embodiment differs from the moving image quality estimation device 20 according to the first embodiment described above in that it assumes the presence of multiple base stations 30 and estimates a resolution distribution for each of multiple areas (cells) of each of the multiple base stations 30.

[0089] Therefore, the network quality information collecting unit 21 collects network quality information for each of the plurality of areas, and the video quality estimating unit 25 estimates the resolution distribution for each of the plurality of areas.

[0090] Hereinafter, an outline of the operation of the moving image quality estimation device 20A according to the second embodiment will be described with reference to FIG. As shown in FIG. 18, in this example, it is assumed that three base stations 30-1 to 30-3 are connected to the MNO network 40.

[0091] The network quality information collecting unit 21 collects network quality information including the frame loss rate, average throughput, etc. of the network in each of the three areas 1 to 3 of the three base stations 30-1 to 30-3. Note that the networks in the three areas 1 to 3 have the same configuration as the MNO network 40 and the network beyond the MNO network 40 when viewed from the three base stations 30-1 to 30-3.

[0092] In the video quality estimation unit 25, for each of the three areas 1 to 3, the policy influence calculation unit 251 calculates R ABRThe loss effect calculation unit 252 calculates R loss The overhead calculation unit 253 calculates R overhead Then, the resolution distribution estimation unit 254 estimates the resolution distribution for each of the three areas 1 to 3. Note that the method for estimating the resolution distribution itself is the same as in the first embodiment described above, and therefore a description thereof will be omitted.

[0093] Furthermore, the resolution distribution estimation unit 254 estimates the average resolution for each of the three areas 1 to 3 based on the average throughput and the resolution distribution. For example, the resolution distribution estimation unit 254 estimates the resolution with the highest ratio in the resolution distribution when the bit rate corresponds to the average throughput as the average resolution. Specifically, assume that the resolution distribution estimated for a certain area is the resolution distribution shown in the lower right diagram of FIG. 16, and that the average throughput for that area is 512 [kbps]. Under this assumption, in the resolution distribution shown in the lower right diagram of FIG. 16, the resolution with the highest ratio when the bit rate corresponds to 512 [kbps], which corresponds to the average throughput, is 240p. Therefore, the resolution distribution estimation unit 254 estimates the average resolution for that area to be 240p.

[0094] Then, the display unit 26 displays the three areas 1 to 3 on the map on the screen of the moving image quality estimation device 20A, and further displays the average resolution of each of the three areas 1 to 3.

[0095] Note that the display example of the display unit 26 in Figure 18 is merely an example and is not limited to this. For example, although the display example in Figure 18 displays the average resolution as video quality, other indicators may be displayed. For example, the average resolution may be displayed in different colors, or by clicking on the display portion of the average resolution, network quality information such as the table shown in Figure 18 may be displayed as details. Alternatively, a resolution distribution such as that shown in Figure 6 may be displayed.

[0096] 18, the display unit 26 displays the moving image quality on the screen of the moving image quality estimation device 20A, but this is not limiting. The display unit 26 may display the moving image quality on any display device other than the moving image quality estimation device 20A (for example, a display device of a network operator, etc.).

[0097] Next, an example of the flow of operations of the moving image quality estimation device 20A according to the second embodiment will be described with reference to Fig. 19. Here, it is assumed that three base stations 30-1 to 30-3 are connected to the MNO network 40, as shown in Fig. 18.

[0098] 19, first, for each of the three areas 1 to 3 of the three base stations 30-1 to 30-3, the processes of steps S201 to S209 similar to steps S101 to S109 in FIG. 16 of the first embodiment are performed. As a result, a resolution distribution is estimated for each of the three areas 1 to 3.

[0099] Next, the resolution distribution estimation unit 254 estimates the average resolution for each of the three areas 1 to 3 based on the average throughput and the resolution distribution (step S210). Thereafter, the display unit 26 displays the three areas 1 to 3 on the map, and further displays the average resolution of each of the three areas 1 to 3 (step S211).

[0100] As described above, according to the second embodiment, the moving image quality estimation unit 25 estimates the resolution distribution and further estimates the average resolution for each of the plurality of areas. The display unit 26 displays each of the plurality of areas on a map, and further displays the average resolution for each of the plurality of areas.

[0101] This makes it possible to grasp the resolution at which video can be provided for each of a plurality of areas. Other effects are the same as those of the first embodiment described above.

[0102] Here, a modification of the second embodiment will be described with reference to FIG. As shown in Fig. 20, in this modification, similar to the example of Fig. 18, it is assumed that three base stations 30-1 to 30-3 are connected to the MNO network 40. It is also assumed that network slice bandwidths are allocated to the three areas 1 to 3 of each of the three base stations 30-1 to 30-3 using network slicing technology.

[0103] The resolution distribution estimation unit 254 estimates the average resolution for each of the three areas 1 to 3. In this case, in an area where there are many terminals 10 present, the bandwidth of the allocated network slice may be insufficient, and the average resolution may become lower than the target resolution.

[0104] Therefore, if there is an area where the average resolution is lower than the target resolution, the resolution distribution estimation unit 254 may increase the bandwidth of the network slice allocated to that area. In this case, the resolution distribution estimation unit 254 may notify the component responsible for allocating the bandwidth of the network slice to each area to increase the bandwidth of the network slice allocated to the certain area.

[0105] It is preferable that the target resolution is common to a plurality of areas, but it may be different for each of the plurality of areas. The target resolution may be stored in advance in the network quality information DB 22, for example.

[0106] <Other embodiments> In the above-described first and second embodiments, the components according to the present disclosure are arranged in one device (the moving image quality estimation device 20, 20A), but this is not limiting. The components in the moving image quality estimation device 20, 20A may be distributed and arranged on a network.

[0107] In addition, in the above-mentioned first and second embodiments, the bit rate corresponding to the resolution to be estimated is estimated by adding the bit rates (1) to (3) below to the bit rate corresponding to the resolution to be estimated, which is included in the video quality information of the video to be estimated, but this is not limited to this. (1) Incremental bit rate R due to the behavior specific to the ABR streaming method ABR (2) Bit rate R for retransmission due to loss loss (3) Bit rate R for header and other overhead overhead

[0108] Even if only one or two of the bit rates (1) to (3) above are added, the estimated resolution distribution is considered to be close to the resolution distribution when actually shaping. Therefore, it is also possible to select one or two of the bit rates (1) to (3) above and add only the selected bit rates. In this case, the amount of calculation can be reduced compared to adding all of the bit rates (1) to (3) above.

[0109] <Concept of the embodiment> Next, with reference to FIG. 21, a configuration example of a moving image quality estimation device 100 conceptually illustrating the moving image quality estimation devices 20 and 20A according to the first and second embodiments will be described.

[0110] The moving image quality estimation device 100 shown in FIG. 21 includes a first collection unit 101, a second collection unit 102, and an estimation unit 103.

[0111] The first collector 101 corresponds to the network quality information collector 21 according to the above-described first and second embodiments. The first collector 101 collects network quality information of a network related to video distribution. The network quality information includes, for example, a frame loss rate, an average throughput, etc. of the network.

[0112] The second collection unit 102 corresponds to the video quality information collection unit 23 according to the above-described first and second embodiments. The second collection unit 102 collects video quality information for each of one or more videos. The video quality information includes, for example, the resolution of the video, a second bit rate of the video corresponding to the resolution, etc.

[0113] The estimation unit 103 corresponds to the moving image quality estimation unit 25 according to the above-described Embodiments 1 and 2. The estimation unit 103 estimates a first bit rate corresponding to the resolution of the moving image based on the network quality information and the moving image quality information.

[0114] At this time, the estimation unit 103 may specify a value to be added to the second bit rate corresponding to the video resolution included in the video quality information based on the network quality information and the video quality information, and estimate the first bit rate corresponding to the video resolution. More specifically, the estimation unit 103 may add the specified value to be added to the second bit rate corresponding to the video resolution included in the video quality information, and estimate the bit rate after the addition as the first bit rate corresponding to the video resolution.

[0115] In addition, when the resolution of the video included in the video quality information is lower than the standard resolution, the estimation unit 103 may add a predetermined bit rate to the second bit rate corresponding to the resolution of the video included in the video quality information as an additional value.

[0116] Furthermore, the estimation unit 103 may calculate a bit rate required for retransmitting video data due to frame loss based on the frame loss rate, and may add the bit rate required for retransmitting video data to a second bit rate corresponding to the video resolution included in the video quality information.

[0117] The estimation unit 103 may also calculate the bit rate required to transmit the header based on the size of the header of the video data packet.The estimation unit 103 may then add the bit rate required to transmit the header to a second bit rate that is included in the video quality information and corresponds to the video resolution.

[0118] Furthermore, when the first bit rate estimated to correspond to the resolution of the video is higher than the average throughput, the estimation unit 103 may adjust the first bit rate to the value of the average throughput.

[0119] In addition, the estimation unit 103 may estimate a first bit rate corresponding to one or more resolutions of one or more videos, and based on the estimation result, estimate a resolution distribution representing the ratio of each resolution corresponding to the first bit rate.

[0120] The moving image quality estimation device 100 may further include a display unit. This display unit corresponds to the display unit 26 according to the second embodiment. The estimation unit 103 may estimate a resolution distribution for each of a plurality of areas and estimate an average resolution based on the estimated resolution distribution and the average throughput. The display unit may then display each of the plurality of areas on a map, and may also display the average resolution for each of the plurality of areas. Alternatively, the display unit may display a first bit rate corresponding to the resolution of the moving image, estimated by the estimation unit 103.

[0121] Furthermore, a network slice bandwidth may be allocated to each of the multiple areas. If an area in which the estimated average resolution is lower than the target resolution exists among the multiple areas, the estimation unit 103 may increase the network slice bandwidth allocated to that area.

[0122] Next, an example of the flow of operations of the moving image quality estimation device 100 shown in FIG. 21 will be described with reference to FIG.

[0123] As shown in FIG. 22, first, the first collector 101 collects network quality information of a network related to video distribution (step S301). Next, the second collection unit 102 collects moving image quality information of the moving image (step S302). Note that steps S301 and S302 do not necessarily have to be performed in this order, but may be performed in the reverse order or simultaneously.

[0124] Thereafter, the estimation unit 103 estimates a bit rate corresponding to the resolution of the video based on the network quality information collected in step S301 and the video quality information collected in step S302 (step S303).

[0125] As described above, in the moving image quality estimation device 100 shown in Fig. 21, the first collection unit 101 collects network quality information of a network related to video distribution. The second collection unit 102 collects video quality information of the video. The estimation unit 103 estimates a bit rate corresponding to the resolution of the video based on the network quality information and the video quality information.

[0126] This allows network operators to determine the bit rate corresponding to a video resolution without having to actually watch the video and collect a huge amount of data, making it possible to understand the relationship between video resolution and bit rate without incurring huge costs.

[0127] Next, with reference to FIG. 23, a configuration example of a moving image quality estimation system including the moving image quality estimation device 100 shown in FIG. 21 will be described. The moving image quality estimation system shown in FIG. 23 includes a terminal 10, a network 110, and a moving image quality estimation device 100.

[0128] The terminal 10 and the moving image quality estimation device 100 are connected to a network 110 . A moving image is distributed to the terminal 10 from a moving image distribution server 80 on a network 110. When the terminal 10 is a mobile terminal, the network 110 is a network consisting of a wireless network between the terminal 10 and the base station 30, a core network, the Internet 70, and a network on the video distribution server 80 side. The core network may be the MNO network 40, or may be the MNO network 40 and the MVNO network 50.

[0129] <Hardware configuration of moving image quality estimation device and moving image quality estimation system according to the embodiment> Next, with reference to FIG. 24, a description will be given of the hardware configuration of a computer 90 that realizes the moving image quality estimation devices 20, 20A according to the above-mentioned first and second embodiments and the moving image quality estimation device 100 according to the concept of the above-mentioned embodiments.

[0130] 24, a computer 90 includes a processor 91, a memory 92, a storage 93, an input / output interface (input / output I / F) 94, and a communication interface (communication I / F) 95. The processor 91, the memory 92, the storage 93, the input / output interface 94, and the communication interface 95 are connected by a data transmission path for transmitting and receiving data to and from each other.

[0131] The processor 91 is, for example, a central processing unit (CPU) or a graphics processing unit (GPU). The memory 92 is, for example, a random access memory (RAM) or a read only memory (ROM). The storage 93 is, for example, a storage device such as a hard disk drive (HDD), a solid state drive (SSD), or a memory card. The storage 93 may also be a memory such as a RAM or a ROM.

[0132] The storage 93 stores programs that realize the functions of the constituent elements of the video quality estimation devices 20, 20A, and 100. The processor 91 executes each of these programs to realize the functions of the constituent elements of the video quality estimation devices 20, 20A, and 100. When executing each of the above programs, the processor 91 may read these programs onto the memory 92 before executing them, or may execute them without reading them onto the memory 92. The memory 92 and the storage 93 also serve to store information and data stored by the constituent elements of the video quality estimation devices 20, 20A, and 100.

[0133] The above-described program can be stored in various types of non-transitory computer-readable media and supplied to a computer (including computer 90). Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Compact Disc-ROMs), CD-Rs (CD-Recordable), CD-R / Ws (CD-ReWritable), semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs). The program can also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can supply the program to a computer via a wired communication path such as an electric wire or optical fiber, or via a wireless communication path.

[0134] The input / output interface 94 is connected to a display device 941, an input device 942, a sound output device 943, etc. The display device 941 is a device that displays a screen corresponding to drawing data processed by the processor 91, such as an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, or a monitor. The input device 942 is a device that accepts operational inputs from an operator, such as a keyboard, a mouse, or a touch sensor. The display device 941 and the input device 942 may be integrated and realized as a touch panel. The sound output device 943 is a device that outputs sound corresponding to the sound data processed by the processor 91, such as a speaker.

[0135] The communication interface 95 transmits and receives data to and from an external device. For example, the communication interface 95 communicates with the external device via a wired communication path or a wireless communication path.

[0136] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0137] Furthermore, some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes. (Appendix 1) a first collection unit that collects network quality information of a network related to video distribution; a second collection unit that collects video quality information of the video; an estimation unit that estimates a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; A video quality estimation device comprising: (Appendix 2) the video quality information includes a resolution of the video and a second bit rate corresponding to the resolution; the estimation unit specifies a value to be added to the second bit rate corresponding to the resolution of the video, which is included in the video quality information, based on the network quality information and the video quality information, and estimates the first bit rate corresponding to the resolution of the video. 2. The video quality estimation device according to claim 1. (Appendix 3) when the resolution of the video included in the video quality information is lower than standard resolution, the estimation unit adds a predetermined bit rate to the second bit rate corresponding to the resolution of the video included in the video quality information as the value to be added. 3. The video quality estimation device according to claim 2. (Appendix 4) the network quality information includes a frame loss rate of the network; The estimation unit calculating a bit rate required for retransmitting video data due to frame loss based on the frame loss rate; adding, as the value to be added, a bit rate required for retransmitting the video data to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 4. The video quality estimation device according to claim 2 or 3. (Appendix 5) The estimation unit Calculating a bit rate required to transmit the header based on the size of the header of the video data packet; adding, as the value to be added, a bit rate required for transmitting the header to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 5. The video quality estimation device according to any one of Supplementary Note 2 to 4. (Appendix 6) the network quality information includes an average throughput of the network; The estimation unit If the first bit rate estimated to correspond to the resolution of the video is higher than the average throughput, adjusting the first bit rate to the value of the average throughput; 6. A moving image quality estimation device according to any one of Supplementary Note 2 to 5. (Appendix 7) further comprising a display unit that displays the first bit rate estimated by the estimation unit and corresponding to the resolution of the video. 7. A moving image quality estimation device according to any one of appendices 1 to 6. (Appendix 8) The estimation unit estimating the first bit rates corresponding to one or more of the resolutions of one or more of the moving images, and estimating a resolution distribution representing a ratio of each resolution corresponding to the first bit rates based on the estimation result; 7. A video quality estimation device according to any one of Supplementary Note 2 to 6. (Appendix 9) Further comprising a display unit, the network quality information includes an average throughput of the network for each of a plurality of areas; the estimation unit estimates the resolution distribution for each of the plurality of areas, and estimates an average resolution based on the estimated resolution distribution and an average throughput; the display unit displays each of the plurality of areas on a map and also displays an average resolution of each of the plurality of areas. 9. The video quality estimation device according to claim 8. (Appendix 10) A bandwidth of a network slice is allocated to each of the plurality of areas, When an area exists among the plurality of areas where the estimated average resolution is lower than a target resolution, the estimation unit increases a bandwidth of a network slice to be allocated to the area. 10. The video quality estimation device according to claim 9. (Appendix 11) A first collection step of collecting network quality information of a network related to video distribution; a second collecting step of collecting video quality information of the video; an estimation step of estimating a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; A video quality estimation method, comprising: (Appendix 12) the video quality information includes a resolution of the video and a second bit rate corresponding to the resolution; In the estimating step, a value to be added to the second bit rate corresponding to the resolution of the video, which is included in the video quality information, is identified based on the network quality information and the video quality information, and the first bit rate corresponding to the resolution of the video is estimated. 12. The video quality estimation method according to claim 11. (Appendix 13) In the estimation step, if the resolution of the video included in the video quality information is lower than standard resolution, a predetermined bit rate is added as the value to be added to the second bit rate corresponding to the resolution of the video included in the video quality information. 13. The video quality estimation method of claim 12. (Appendix 14) the network quality information includes a frame loss rate of the network; In the estimation step, calculating a bit rate required for retransmitting video data due to frame loss based on the frame loss rate; adding, as the value to be added, a bit rate required for retransmitting the video data to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 14. The video quality estimation method according to claim 12 or 13. (Appendix 15) In the estimation step, Calculating a bit rate required to transmit the header based on the size of the header of the video data packet; adding, as the value to be added, a bit rate required for transmitting the header to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 15. A video quality estimation method according to any one of appendices 12 to 14. (Appendix 16) the network quality information includes an average throughput of the network; In the estimation step, If the first bit rate estimated to correspond to the resolution of the video is higher than the average throughput, adjusting the first bit rate to the value of the average throughput; 16. A video quality estimation method according to any one of appendices 12 to 15. (Appendix 17) a display step of displaying the first bit rate estimated in the estimation step and corresponding to the resolution of the video. 17. A video quality estimation method according to any one of appendices 11 to 16. (Appendix 18) In the estimation step, estimating the first bit rates corresponding to one or more of the resolutions of one or more of the moving images, and estimating a resolution distribution representing a ratio of each resolution corresponding to the first bit rates based on the estimation result; 17. A video quality estimation method according to any one of appendices 12 to 16. (Appendix 19) the network quality information includes an average throughput of the network for each of a plurality of areas; In the estimation step, the resolution distribution is estimated for each of the plurality of areas, and an average resolution is estimated based on the estimated resolution distribution and an average throughput; The video quality estimation method includes: a display step of displaying each of the plurality of areas on a map and displaying an average resolution of each of the plurality of areas, 19. The video quality estimation method of claim 18. (Appendix 20) A bandwidth of a network slice is allocated to each of the plurality of areas, In the estimation step, if there is an area among the plurality of areas where the estimated average resolution is lower than a target resolution, a bandwidth of the network slice allocated to the area is increased. 19. The video quality estimation method of claim 19. (Appendix 21) a first collection unit that collects network quality information of a network related to video distribution; a second collection unit that collects video quality information of the video; an estimation unit that estimates a first bit rate corresponding to a resolution of the video based on the network quality information and the video quality information; A video quality estimation system comprising: (Appendix 22) the video quality information includes a resolution of the video and a second bit rate corresponding to the resolution; the estimation unit specifies a value to be added to the second bit rate corresponding to the resolution of the video, which is included in the video quality information, based on the network quality information and the video quality information, and estimates the first bit rate corresponding to the resolution of the video. 22. The video quality estimation system of claim 21. (Appendix 23) when the resolution of the video included in the video quality information is lower than standard resolution, the estimation unit adds a predetermined bit rate to the second bit rate corresponding to the resolution of the video included in the video quality information as the value to be added. 23. The video quality estimation system of claim 22. (Appendix 24) the network quality information includes a frame loss rate of the network; The estimation unit calculating a bit rate required for retransmitting video data due to frame loss based on the frame loss rate; adding, as the value to be added, a bit rate required for retransmitting the video data to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 24. The video quality estimation system according to claim 22 or 23. (Appendix 25) The estimation unit Calculating a bit rate required to transmit the header based on the size of the header of the video data packet; adding, as the value to be added, a bit rate required for transmitting the header to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; 25. A video quality estimation system according to any one of appendices 22 to 24. (Appendix 26) the network quality information includes an average throughput of the network; The estimation unit If the first bit rate estimated to correspond to the resolution of the video is higher than the average throughput, adjusting the first bit rate to the value of the average throughput; 26. A video quality estimation system according to any one of appendices 22 to 25. (Appendix 27) further comprising a display unit that displays the first bit rate estimated by the estimation unit and corresponding to the resolution of the video. 27. A video quality estimation system according to any one of appendices 21 to 26. (Appendix 28) The estimation unit estimating the first bit rates corresponding to one or more of the resolutions of one or more of the moving images, and estimating a resolution distribution representing a ratio of each resolution corresponding to the first bit rates based on the estimation result; 27. A video quality estimation system according to any one of appendices 22 to 26. (Appendix 29) Further comprising a display unit, the network quality information includes an average throughput of the network for each of a plurality of areas; the estimation unit estimates the resolution distribution for each of the plurality of areas, and estimates an average resolution based on the estimated resolution distribution and an average throughput; the display unit displays each of the plurality of areas on a map and also displays an average resolution of each of the plurality of areas. 29. The video quality estimation system of claim 28. (Appendix 30) A bandwidth of a network slice is allocated to each of the plurality of areas, When an area exists among the plurality of areas where the estimated average resolution is lower than a target resolution, the estimation unit increases a bandwidth of a network slice to be allocated to the area. 29. The video quality estimation system of claim 29. [Explanation of symbols]

[0138] 10 devices 20,20A Video quality estimation device 21 Network Quality Information Collection Department 22 Network Quality Information DB 23 Video Quality Information Collection Department 24 Video Quality Information DB 25 Video quality estimation unit 251 Policy Impact Calculation Unit 252 Loss Impact Calculation Unit 253 Overhead Calculation Unit 254 Resolution distribution estimation section 26 Display section 30 base station 40 MNO networks 41 S-GW 42 P-GW 43 MME 44 HSS 50 MVNO networks 51 P-GW 52 PCRF 53 Authentication Server 60 Network Tunnels 70 Internet 80 Video distribution server 90 Computer 91 processors 92 memory 93 Storage 94 Input / Output Interface 941 Display device 942 Input Device 943 Sound Output Device 95 Communication Interface 100 Video quality estimation device 101 First Collection Section 102 Second Collection Section 103 Estimation part 110 Network

Claims

1. an estimation unit that estimates a first bit rate corresponding to a resolution of a video based on network quality information of a network related to video distribution and video quality information including a resolution of the video and a second bit rate corresponding to the resolution of the video; a calculation unit that calculates a value to be added to the second bit rate; Equipped with The estimation unit adding the value to be added to the second bit rate to estimate the first bit rate; estimating the first bit rates corresponding to one or more of the resolutions of one or more of the moving images, and estimating a resolution distribution representing a ratio of each resolution corresponding to the first bit rates based on the estimation result; Video quality estimation system.

2. when the resolution of the video included in the video quality information is lower than standard resolution, the estimation unit adds a predetermined bit rate to the second bit rate corresponding to the resolution of the video included in the video quality information as the value to be added. The video quality estimation system according to claim 1 .

3. the network quality information includes a frame loss rate of the network; The estimation unit calculating a bit rate required for retransmitting video data due to frame loss based on the frame loss rate; adding, as the value to be added, a bit rate required for retransmitting the video data to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; The video quality estimation system according to claim 1 or 2.

4. The estimation unit Calculating a bit rate required to transmit the header based on the size of the header of the video data packet; adding, as the value to be added, a bit rate required for transmitting the header to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; The video quality estimation system according to claim 1 .

5. an estimation step of estimating a first bit rate corresponding to a resolution of the video based on network quality information of a network related to video distribution and video quality information including a resolution of the video and a second bit rate corresponding to the resolution of the video; a calculation step of calculating a value to be added to the second bit rate; Including, In the estimation step, adding the value to be added to the second bit rate to estimate the first bit rate; estimating the first bit rates corresponding to one or more of the resolutions of one or more of the moving images, and estimating a resolution distribution representing a ratio of each resolution corresponding to the first bit rates based on the estimation result; Video quality estimation method.

6. In the estimation step, if the resolution of the moving image included in the moving image quality information is lower than standard resolution, a predetermined bit rate is added as the value to be added to the second bit rate corresponding to the resolution of the moving image included in the moving image quality information. The method for estimating moving image quality according to claim 5 .

7. the network quality information includes a frame loss rate of the network; In the estimation step, calculating a bit rate required for retransmitting video data due to frame loss based on the frame loss rate; adding, as the value to be added, a bit rate required for retransmitting the video data to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; The moving image quality estimation method according to claim 5 or 6.

8. In the estimation step, Calculating a bit rate required to transmit the header based on the size of the header of the video data packet; adding, as the value to be added, a bit rate required for transmitting the header to the second bit rate corresponding to the resolution of the video, which is included in the video quality information; The moving image quality estimation method according to any one of claims 5 to 7.

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